Overview

Dataset statistics

Number of variables304
Number of observations17992
Missing cells1236103
Missing cells (%)22.6%
Total size in memory41.9 MiB
Average record size in memory2.4 KiB

Variable types

Text69
Numeric90
Boolean42
Unsupported103

Alerts

is_dependent_in_at_least_1_policy has constant value ""Constant
f_ever_declined_la has constant value ""Constant
ape_gi_42e115 has constant value ""Constant
ape_ltc_1280bf has constant value ""Constant
ape_inv_dcd836 has constant value ""Constant
ape_lh_d0adeb has constant value ""Constant
ape_gi_a10d1b has constant value ""Constant
ape_gi_29d435 has constant value ""Constant
ape_gi_856320 has constant value ""Constant
ape_gi_058815 has constant value ""Constant
ape_32c74c has constant value ""Constant
sumins_gi_42e115 has constant value ""Constant
sumins_ltc_1280bf has constant value ""Constant
sumins_inv_dcd836 has constant value ""Constant
sumins_lh_d0adeb has constant value ""Constant
sumins_grp_22decf has constant value ""Constant
sumins_gi_a10d1b has constant value ""Constant
sumins_gi_29d435 has constant value ""Constant
sumins_lh_e22a6a has constant value ""Constant
sumins_grp_e04c3a has constant value ""Constant
sumins_gi_856320 has constant value ""Constant
sumins_grp_94baec has constant value ""Constant
sumins_gi_058815 has constant value ""Constant
sumins_32c74c has constant value ""Constant
prempaid_gi_42e115 has constant value ""Constant
prempaid_ltc_1280bf has constant value ""Constant
prempaid_inv_dcd836 has constant value ""Constant
prempaid_lh_d0adeb has constant value ""Constant
prempaid_gi_a10d1b has constant value ""Constant
prempaid_gi_29d435 has constant value ""Constant
prempaid_gi_856320 has constant value ""Constant
prempaid_gi_058815 has constant value ""Constant
prempaid_32c74c has constant value ""Constant
ape_d0adeb has constant value ""Constant
ape_gi has constant value ""Constant
f_hold_d0adeb has constant value ""Constant
f_hold_gi has constant value ""Constant
sumins_e22a6a has constant value ""Constant
sumins_d0adeb has constant value ""Constant
sumins_gi has constant value ""Constant
prempaid_d0adeb has constant value ""Constant
prempaid_gi has constant value ""Constant
lapse_ape_ltc_1280bf has constant value ""Constant
lapse_ape_inv_dcd836 has constant value ""Constant
lapse_ape_lh_d0adeb has constant value ""Constant
lapse_ape_32c74c has constant value ""Constant
n_months_since_lapse_ltc_1280bf has constant value ""Constant
n_months_since_lapse_inv_dcd836 has constant value ""Constant
n_months_since_lapse_lh_d0adeb has constant value ""Constant
n_months_since_lapse_32c74c has constant value ""Constant
f_ever_bought_d0adeb has constant value ""Constant
n_months_last_bought_d0adeb has constant value ""Constant
f_ever_bought_ltc_1280bf has constant value ""Constant
f_ever_bought_inv_dcd836 has constant value ""Constant
f_ever_bought_lh_d0adeb has constant value ""Constant
f_ever_bought_32c74c has constant value ""Constant
n_months_last_bought_ltc_1280bf has constant value ""Constant
n_months_last_bought_inv_dcd836 has constant value ""Constant
n_months_last_bought_lh_d0adeb has constant value ""Constant
flg_affconnect_show_interest_ever has constant value ""Constant
flg_affconnect_ready_to_buy_ever has constant value ""Constant
flg_hlthclaim_839f8a_ever has constant value ""Constant
flg_hlthclaim_14cb37_ever has constant value ""Constant
f_purchase_lh has constant value ""Constant
race_desc has 3996 (22.2%) missing valuesMissing
flg_substandard has 1014 (5.6%) missing valuesMissing
flg_is_borderline_standard has 1014 (5.6%) missing valuesMissing
flg_is_revised_term has 1014 (5.6%) missing valuesMissing
flg_is_rental_flat has 1014 (5.6%) missing valuesMissing
flg_has_health_claim has 1014 (5.6%) missing valuesMissing
flg_has_life_claim has 1014 (5.6%) missing valuesMissing
flg_gi_claim has 1014 (5.6%) missing valuesMissing
flg_is_proposal has 1014 (5.6%) missing valuesMissing
flg_with_preauthorisation has 1014 (5.6%) missing valuesMissing
flg_is_returned_mail has 1014 (5.6%) missing valuesMissing
is_consent_to_mail has 1014 (5.6%) missing valuesMissing
is_consent_to_email has 1014 (5.6%) missing valuesMissing
is_consent_to_call has 1014 (5.6%) missing valuesMissing
is_consent_to_sms has 1014 (5.6%) missing valuesMissing
is_valid_dm has 1014 (5.6%) missing valuesMissing
is_valid_email has 1014 (5.6%) missing valuesMissing
is_housewife_retiree has 1014 (5.6%) missing valuesMissing
is_sg_pr has 1014 (5.6%) missing valuesMissing
is_class_1_2 has 1014 (5.6%) missing valuesMissing
is_dependent_in_at_least_1_policy has 1014 (5.6%) missing valuesMissing
f_ever_declined_la has 16759 (93.1%) missing valuesMissing
hh_20 has 2809 (15.6%) missing valuesMissing
pop_20 has 2809 (15.6%) missing valuesMissing
hh_size has 2809 (15.6%) missing valuesMissing
hh_size_est has 2809 (15.6%) missing valuesMissing
annual_income_est has 2809 (15.6%) missing valuesMissing
recency_lapse has 12592 (70.0%) missing valuesMissing
recency_cancel has 17368 (96.5%) missing valuesMissing
tot_cancel_pols has 17368 (96.5%) missing valuesMissing
lapse_ape_ltc_1280bf has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_6fc3e6 has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_de05ae has 12592 (70.0%) missing valuesMissing
lapse_ape_inv_dcd836 has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_945b5a has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_6a5788 has 12592 (70.0%) missing valuesMissing
lapse_ape_ltc_43b9d5 has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_9cdedf has 12592 (70.0%) missing valuesMissing
lapse_ape_lh_d0adeb has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_1581d7 has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_22decf has 12592 (70.0%) missing valuesMissing
lapse_ape_lh_507c37 has 12592 (70.0%) missing valuesMissing
lapse_ape_lh_839f8a has 12592 (70.0%) missing valuesMissing
lapse_ape_inv_e9f316 has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_caa6ff has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_fd3bfb has 12592 (70.0%) missing valuesMissing
lapse_ape_lh_e22a6a has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_70e1dd has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_e04c3a has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_fe5fb8 has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_94baec has 12592 (70.0%) missing valuesMissing
lapse_ape_grp_e91421 has 12592 (70.0%) missing valuesMissing
lapse_ape_lh_f852af has 12592 (70.0%) missing valuesMissing
lapse_ape_lh_947b15 has 12592 (70.0%) missing valuesMissing
lapse_ape_32c74c has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_ltc_1280bf has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_6fc3e6 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_de05ae has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_inv_dcd836 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_945b5a has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_6a5788 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_ltc_43b9d5 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_9cdedf has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_lh_d0adeb has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_1581d7 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_22decf has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_lh_507c37 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_lh_839f8a has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_inv_e9f316 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_caa6ff has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_fd3bfb has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_lh_e22a6a has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_70e1dd has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_e04c3a has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_fe5fb8 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_94baec has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_grp_e91421 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_lh_f852af has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_lh_947b15 has 12592 (70.0%) missing valuesMissing
n_months_since_lapse_32c74c has 12592 (70.0%) missing valuesMissing
flg_affconnect_show_interest_ever has 17497 (97.2%) missing valuesMissing
flg_affconnect_ready_to_buy_ever has 17178 (95.5%) missing valuesMissing
flg_affconnect_lapse_ever has 17178 (95.5%) missing valuesMissing
affcon_visit_days has 17178 (95.5%) missing valuesMissing
n_months_since_visit_affcon has 17178 (95.5%) missing valuesMissing
clmcon_visit_days has 17203 (95.6%) missing valuesMissing
recency_clmcon has 17203 (95.6%) missing valuesMissing
recency_clmcon_regis has 17203 (95.6%) missing valuesMissing
hlthclaim_amt has 16543 (91.9%) missing valuesMissing
recency_hlthclaim has 16543 (91.9%) missing valuesMissing
hlthclaim_cnt_success has 16715 (92.9%) missing valuesMissing
recency_hlthclaim_success has 16715 (92.9%) missing valuesMissing
hlthclaim_cnt_unsuccess has 17382 (96.6%) missing valuesMissing
recency_hlthclaim_unsuccess has 17382 (96.6%) missing valuesMissing
flg_hlthclaim_839f8a_ever has 17707 (98.4%) missing valuesMissing
recency_hlthclaim_839f8a has 17707 (98.4%) missing valuesMissing
flg_hlthclaim_14cb37_ever has 16617 (92.4%) missing valuesMissing
recency_hlthclaim_14cb37 has 16617 (92.4%) missing valuesMissing
giclaim_amt has 17544 (97.5%) missing valuesMissing
recency_giclaim has 17544 (97.5%) missing valuesMissing
giclaim_cnt_success has 17992 (100.0%) missing valuesMissing
recency_giclaim_success has 17992 (100.0%) missing valuesMissing
giclaim_cnt_unsuccess has 17992 (100.0%) missing valuesMissing
recency_giclaim_unsuccess has 17992 (100.0%) missing valuesMissing
flg_gi_claim_29d435_ever has 17992 (100.0%) missing valuesMissing
flg_gi_claim_058815_ever has 17992 (100.0%) missing valuesMissing
flg_gi_claim_42e115_ever has 17992 (100.0%) missing valuesMissing
flg_gi_claim_856320_ever has 17992 (100.0%) missing valuesMissing
f_purchase_lh has 17282 (96.1%) missing valuesMissing
flg_is_revised_term is highly skewed (γ1 = 37.57770017)Skewed
f_ever_bought_grp_de05ae is highly skewed (γ1 = 50.67261823)Skewed
clntnum has unique valuesUnique
ape_grp_6fc3e6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_de05ae is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_945b5a is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_6a5788 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_ltc_43b9d5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_9cdedf is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_1581d7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_22decf is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_lh_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_lh_839f8a is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_inv_e9f316 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_caa6ff is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_fd3bfb is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_lh_e22a6a is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_70e1dd is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_e04c3a is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_fe5fb8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_94baec is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_grp_e91421 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_lh_f852af is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_lh_947b15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_6fc3e6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_de05ae is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_945b5a is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_6a5788 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_ltc_43b9d5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_9cdedf is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_1581d7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_lh_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_inv_e9f316 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_caa6ff is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_fd3bfb is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_70e1dd is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_fe5fb8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_grp_e91421 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_lh_f852af is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_lh_947b15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_6fc3e6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_de05ae is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_945b5a is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_6a5788 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_ltc_43b9d5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_9cdedf is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_1581d7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_22decf is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_lh_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_lh_839f8a is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_inv_e9f316 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_caa6ff is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_fd3bfb is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_lh_e22a6a is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_70e1dd is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_e04c3a is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_fe5fb8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_94baec is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_grp_e91421 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_lh_f852af is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_lh_947b15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_839f8a is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_e22a6a is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_c4bda5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_ltc is an unsupported type, check if it needs cleaning or further analysisUnsupported
ape_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_839f8a is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_c4bda5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_ltc is an unsupported type, check if it needs cleaning or further analysisUnsupported
sumins_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_839f8a is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_e22a6a is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_c4bda5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_ltc is an unsupported type, check if it needs cleaning or further analysisUnsupported
prempaid_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_6fc3e6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_de05ae is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_945b5a is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_6a5788 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_ltc_43b9d5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_9cdedf is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_1581d7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_22decf is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_lh_507c37 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_lh_839f8a is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_inv_e9f316 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_caa6ff is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_fd3bfb is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_lh_e22a6a is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_70e1dd is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_e04c3a is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_fe5fb8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_94baec is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_grp_e91421 is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_lh_f852af is an unsupported type, check if it needs cleaning or further analysisUnsupported
lapse_ape_lh_947b15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
hlthclaim_amt is an unsupported type, check if it needs cleaning or further analysisUnsupported
giclaim_amt is an unsupported type, check if it needs cleaning or further analysisUnsupported
giclaim_cnt_success is an unsupported type, check if it needs cleaning or further analysisUnsupported
recency_giclaim_success is an unsupported type, check if it needs cleaning or further analysisUnsupported
giclaim_cnt_unsuccess is an unsupported type, check if it needs cleaning or further analysisUnsupported
recency_giclaim_unsuccess is an unsupported type, check if it needs cleaning or further analysisUnsupported
flg_gi_claim_29d435_ever is an unsupported type, check if it needs cleaning or further analysisUnsupported
flg_gi_claim_058815_ever is an unsupported type, check if it needs cleaning or further analysisUnsupported
flg_gi_claim_42e115_ever is an unsupported type, check if it needs cleaning or further analysisUnsupported
flg_gi_claim_856320_ever is an unsupported type, check if it needs cleaning or further analysisUnsupported
flg_substandard has 16294 (90.6%) zerosZeros
flg_is_borderline_standard has 16416 (91.2%) zerosZeros
flg_is_revised_term has 16966 (94.3%) zerosZeros
flg_is_rental_flat has 16765 (93.2%) zerosZeros
flg_has_health_claim has 16150 (89.8%) zerosZeros
flg_has_life_claim has 16917 (94.0%) zerosZeros
flg_gi_claim has 16413 (91.2%) zerosZeros
flg_is_proposal has 16929 (94.1%) zerosZeros
flg_with_preauthorisation has 16838 (93.6%) zerosZeros
flg_is_returned_mail has 16651 (92.5%) zerosZeros
is_consent_to_mail has 11515 (64.0%) zerosZeros
is_consent_to_email has 10476 (58.2%) zerosZeros
is_consent_to_call has 14955 (83.1%) zerosZeros
is_consent_to_sms has 12665 (70.4%) zerosZeros
is_valid_dm has 4859 (27.0%) zerosZeros
is_valid_email has 4356 (24.2%) zerosZeros
is_housewife_retiree has 16924 (94.1%) zerosZeros
is_sg_pr has 1385 (7.7%) zerosZeros
is_class_1_2 has 7927 (44.1%) zerosZeros
is_dependent_in_at_least_1_policy has 16978 (94.4%) zerosZeros
flg_latest_being_lapse has 16258 (90.4%) zerosZeros
flg_latest_being_cancel has 17861 (99.3%) zerosZeros
f_hold_839f8a has 16043 (89.2%) zerosZeros
f_hold_e22a6a has 14288 (79.4%) zerosZeros
f_hold_d0adeb has 17992 (100.0%) zerosZeros
f_hold_c4bda5 has 17914 (99.6%) zerosZeros
f_hold_ltc has 14643 (81.4%) zerosZeros
f_hold_507c37 has 11824 (65.7%) zerosZeros
f_hold_gi has 17992 (100.0%) zerosZeros
f_ever_bought_839f8a has 15848 (88.1%) zerosZeros
f_ever_bought_e22a6a has 13377 (74.3%) zerosZeros
f_ever_bought_d0adeb has 17992 (100.0%) zerosZeros
f_ever_bought_c4bda5 has 17910 (99.5%) zerosZeros
f_ever_bought_ltc has 14581 (81.0%) zerosZeros
f_ever_bought_507c37 has 10991 (61.1%) zerosZeros
f_ever_bought_gi has 15882 (88.3%) zerosZeros
f_ever_bought_ltc_1280bf has 17992 (100.0%) zerosZeros
f_ever_bought_grp_6fc3e6 has 17600 (97.8%) zerosZeros
f_ever_bought_grp_de05ae has 17985 (> 99.9%) zerosZeros
f_ever_bought_inv_dcd836 has 17992 (100.0%) zerosZeros
f_ever_bought_grp_945b5a has 16590 (92.2%) zerosZeros
f_ever_bought_grp_6a5788 has 17862 (99.3%) zerosZeros
f_ever_bought_ltc_43b9d5 has 14689 (81.6%) zerosZeros
f_ever_bought_grp_9cdedf has 17462 (97.1%) zerosZeros
f_ever_bought_lh_d0adeb has 17992 (100.0%) zerosZeros
f_ever_bought_grp_1581d7 has 14836 (82.5%) zerosZeros
f_ever_bought_grp_22decf has 17902 (99.5%) zerosZeros
f_ever_bought_lh_507c37 has 16558 (92.0%) zerosZeros
f_ever_bought_lh_839f8a has 17326 (96.3%) zerosZeros
f_ever_bought_inv_e9f316 has 17910 (99.5%) zerosZeros
f_ever_bought_grp_caa6ff has 17270 (96.0%) zerosZeros
f_ever_bought_grp_fd3bfb has 17776 (98.8%) zerosZeros
f_ever_bought_lh_e22a6a has 14511 (80.7%) zerosZeros
f_ever_bought_grp_70e1dd has 13982 (77.7%) zerosZeros
f_ever_bought_grp_e04c3a has 17809 (99.0%) zerosZeros
f_ever_bought_grp_fe5fb8 has 16555 (92.0%) zerosZeros
f_ever_bought_grp_94baec has 16686 (92.7%) zerosZeros
f_ever_bought_grp_e91421 has 17714 (98.5%) zerosZeros
f_ever_bought_lh_f852af has 16519 (91.8%) zerosZeros
f_ever_bought_lh_947b15 has 16428 (91.3%) zerosZeros
f_ever_bought_32c74c has 17992 (100.0%) zerosZeros
f_elx has 13852 (77.0%) zerosZeros
f_mindef_mha has 15460 (85.9%) zerosZeros
f_retail has 4376 (24.3%) zerosZeros
flg_affconnect_lapse_ever has 805 (4.5%) zerosZeros
n_months_since_visit_affcon has 219 (1.2%) zerosZeros
recency_clmcon has 304 (1.7%) zerosZeros

Reproduction

Analysis started2024-01-27 06:09:04.166073
Analysis finished2024-01-27 06:09:09.342714
Duration5.18 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

clntnum
Text

UNIQUE 

Distinct17992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:09.838675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters179920
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17992 ?
Unique (%)100.0%

Sample

1st row91b546e924
2nd row896bae548c
3rd rowf364439ae6
4th row70f319cfe1
5th row2647a81328
ValueCountFrequency (%)
91b546e924 1
 
< 0.1%
1905ef55fd 1
 
< 0.1%
2647a81328 1
 
< 0.1%
8df857fd8e 1
 
< 0.1%
b57b978719 1
 
< 0.1%
4b1cb3726c 1
 
< 0.1%
390a33cf6c 1
 
< 0.1%
9001981079 1
 
< 0.1%
a71d8a0660 1
 
< 0.1%
f364439ae6 1
 
< 0.1%
Other values (17982) 17982
99.9%
2024-01-27T14:09:10.202033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 11505
 
6.4%
9 11390
 
6.3%
7 11354
 
6.3%
f 11314
 
6.3%
a 11298
 
6.3%
1 11276
 
6.3%
6 11238
 
6.2%
0 11233
 
6.2%
5 11226
 
6.2%
2 11196
 
6.2%
Other values (6) 66890
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112347
62.4%
Lowercase Letter 67573
37.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 11390
10.1%
7 11354
10.1%
1 11276
10.0%
6 11238
10.0%
0 11233
10.0%
5 11226
10.0%
2 11196
10.0%
3 11169
9.9%
4 11135
9.9%
8 11130
9.9%
Lowercase Letter
ValueCountFrequency (%)
c 11505
17.0%
f 11314
16.7%
a 11298
16.7%
d 11177
16.5%
b 11159
16.5%
e 11120
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 112347
62.4%
Latin 67573
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9 11390
10.1%
7 11354
10.1%
1 11276
10.0%
6 11238
10.0%
0 11233
10.0%
5 11226
10.0%
2 11196
10.0%
3 11169
9.9%
4 11135
9.9%
8 11130
9.9%
Latin
ValueCountFrequency (%)
c 11505
17.0%
f 11314
16.7%
a 11298
16.7%
d 11177
16.5%
b 11159
16.5%
e 11120
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 11505
 
6.4%
9 11390
 
6.3%
7 11354
 
6.3%
f 11314
 
6.3%
a 11298
 
6.3%
1 11276
 
6.3%
6 11238
 
6.2%
0 11233
 
6.2%
5 11226
 
6.2%
2 11196
 
6.2%
Other values (6) 66890
37.2%

race_desc
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing3996
Missing (%)22.2%
Memory size281.1 KiB
2024-01-27T14:09:10.322454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.685338668
Min length5

Characters and Unicode

Total characters93568
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChinese
2nd rowChinese
3rd rowOthers
4th rowChinese
5th rowChinese
ValueCountFrequency (%)
chinese 10520
75.2%
others 1699
 
12.1%
malay 928
 
6.6%
indian 849
 
6.1%
2024-01-27T14:09:10.540989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22739
24.3%
h 12219
13.1%
s 12219
13.1%
n 12218
13.1%
i 11369
12.2%
C 10520
11.2%
a 2705
 
2.9%
O 1699
 
1.8%
t 1699
 
1.8%
r 1699
 
1.8%
Other values (5) 4482
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79572
85.0%
Uppercase Letter 13996
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22739
28.6%
h 12219
15.4%
s 12219
15.4%
n 12218
15.4%
i 11369
14.3%
a 2705
 
3.4%
t 1699
 
2.1%
r 1699
 
2.1%
l 928
 
1.2%
y 928
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C 10520
75.2%
O 1699
 
12.1%
M 928
 
6.6%
I 849
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 93568
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22739
24.3%
h 12219
13.1%
s 12219
13.1%
n 12218
13.1%
i 11369
12.2%
C 10520
11.2%
a 2705
 
2.9%
O 1699
 
1.8%
t 1699
 
1.8%
r 1699
 
1.8%
Other values (5) 4482
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22739
24.3%
h 12219
13.1%
s 12219
13.1%
n 12218
13.1%
i 11369
12.2%
C 10520
11.2%
a 2705
 
2.9%
O 1699
 
1.8%
t 1699
 
1.8%
r 1699
 
1.8%
Other values (5) 4482
 
4.8%
Distinct26
Distinct (%)0.1%
Missing20
Missing (%)0.1%
Memory size281.1 KiB
2024-01-27T14:09:10.653666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length9
Mean length9.00817939
Min length5

Characters and Unicode

Total characters161895
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st rowSingapore
2nd rowSingapore
3rd rowSingapore
4th rowSingapore
5th rowSingapore
ValueCountFrequency (%)
singapore 17789
98.7%
malaysia 85
 
0.5%
not 20
 
0.1%
applicable 20
 
0.1%
indonesia 18
 
0.1%
united 17
 
0.1%
australia 10
 
0.1%
kingdom 9
 
< 0.1%
states 5
 
< 0.1%
china 4
 
< 0.1%
Other values (26) 50
 
0.3%
2024-01-27T14:09:10.904165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18151
11.2%
i 17976
11.1%
n 17893
11.1%
e 17870
11.0%
o 17853
11.0%
p 17838
11.0%
r 17817
11.0%
g 17803
11.0%
S 17798
11.0%
l 146
 
0.1%
Other values (36) 750
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 143793
88.8%
Uppercase Letter 18034
 
11.1%
Space Separator 55
 
< 0.1%
Other Punctuation 6
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18151
12.6%
i 17976
12.5%
n 17893
12.4%
e 17870
12.4%
o 17853
12.4%
p 17838
12.4%
r 17817
12.4%
g 17803
12.4%
l 146
 
0.1%
s 128
 
0.1%
Other values (13) 318
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S 17798
98.7%
M 85
 
0.5%
A 35
 
0.2%
N 22
 
0.1%
I 20
 
0.1%
U 20
 
0.1%
C 14
 
0.1%
K 11
 
0.1%
T 6
 
< 0.1%
E 3
 
< 0.1%
Other values (8) 20
 
0.1%
Space Separator
ValueCountFrequency (%)
55
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161827
> 99.9%
Common 68
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18151
11.2%
i 17976
11.1%
n 17893
11.1%
e 17870
11.0%
o 17853
11.0%
p 17838
11.0%
r 17817
11.0%
g 17803
11.0%
S 17798
11.0%
l 146
 
0.1%
Other values (31) 682
 
0.4%
Common
ValueCountFrequency (%)
55
80.9%
. 6
 
8.8%
) 3
 
4.4%
( 3
 
4.4%
- 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18151
11.2%
i 17976
11.1%
n 17893
11.1%
e 17870
11.0%
o 17853
11.0%
p 17838
11.0%
r 17817
11.0%
g 17803
11.0%
S 17798
11.0%
l 146
 
0.1%
Other values (36) 750
 
0.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:10.985804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17992
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowP
3rd rowP
4th rowP
5th rowP
ValueCountFrequency (%)
p 14657
81.5%
g 3311
 
18.4%
c 24
 
0.1%
2024-01-27T14:09:11.170680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 14657
81.5%
G 3311
 
18.4%
C 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17992
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 14657
81.5%
G 3311
 
18.4%
C 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 17992
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 14657
81.5%
G 3311
 
18.4%
C 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 14657
81.5%
G 3311
 
18.4%
C 24
 
0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:11.251220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.000666963
Min length6

Characters and Unicode

Total characters107964
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTIVE
2nd rowACTIVE
3rd rowACTIVE
4th rowACTIVE
5th rowACTIVE
ValueCountFrequency (%)
active 17205
95.6%
lapsed 775
 
4.3%
matured 12
 
0.1%
2024-01-27T14:09:11.453080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 17992
16.7%
E 17992
16.7%
T 17217
15.9%
C 17205
15.9%
I 17205
15.9%
V 17205
15.9%
D 787
 
0.7%
L 775
 
0.7%
P 775
 
0.7%
S 775
 
0.7%
Other values (3) 36
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 107964
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17992
16.7%
E 17992
16.7%
T 17217
15.9%
C 17205
15.9%
I 17205
15.9%
V 17205
15.9%
D 787
 
0.7%
L 775
 
0.7%
P 775
 
0.7%
S 775
 
0.7%
Other values (3) 36
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 107964
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17992
16.7%
E 17992
16.7%
T 17217
15.9%
C 17205
15.9%
I 17205
15.9%
V 17205
15.9%
D 787
 
0.7%
L 775
 
0.7%
P 775
 
0.7%
S 775
 
0.7%
Other values (3) 36
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17992
16.7%
E 17992
16.7%
T 17217
15.9%
C 17205
15.9%
I 17205
15.9%
V 17205
15.9%
D 787
 
0.7%
L 775
 
0.7%
P 775
 
0.7%
S 775
 
0.7%
Other values (3) 36
 
< 0.1%
Distinct4576
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:11.638649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.996665185
Min length4

Characters and Unicode

Total characters179860
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2253 ?
Unique (%)12.5%

Sample

1st row2017-10-31
2nd row2007-05-23
3rd row2019-08-31
4th row2021-10-18
5th row2018-07-20
ValueCountFrequency (%)
2023-01-01 1168
 
6.5%
2016-12-01 180
 
1.0%
2022-08-01 178
 
1.0%
2016-10-01 159
 
0.9%
2023-04-01 143
 
0.8%
2022-07-01 109
 
0.6%
2019-07-01 82
 
0.5%
2018-05-01 81
 
0.5%
2009-01-01 77
 
0.4%
2020-11-24 65
 
0.4%
Other values (4566) 15750
87.5%
2024-01-27T14:09:12.075376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 48150
26.8%
- 35964
20.0%
2 33472
18.6%
1 30368
16.9%
3 7907
 
4.4%
9 4565
 
2.5%
8 4150
 
2.3%
5 4101
 
2.3%
6 3878
 
2.2%
7 3728
 
2.1%
Other values (5) 3577
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143856
80.0%
Dash Punctuation 35964
 
20.0%
Lowercase Letter 30
 
< 0.1%
Uppercase Letter 10
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48150
33.5%
2 33472
23.3%
1 30368
21.1%
3 7907
 
5.5%
9 4565
 
3.2%
8 4150
 
2.9%
5 4101
 
2.9%
6 3878
 
2.7%
7 3728
 
2.6%
4 3537
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
o 10
33.3%
n 10
33.3%
e 10
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 35964
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 179820
> 99.9%
Latin 40
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48150
26.8%
- 35964
20.0%
2 33472
18.6%
1 30368
16.9%
3 7907
 
4.4%
9 4565
 
2.5%
8 4150
 
2.3%
5 4101
 
2.3%
6 3878
 
2.2%
7 3728
 
2.1%
Latin
ValueCountFrequency (%)
N 10
25.0%
o 10
25.0%
n 10
25.0%
e 10
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48150
26.8%
- 35964
20.0%
2 33472
18.6%
1 30368
16.9%
3 7907
 
4.4%
9 4565
 
2.5%
8 4150
 
2.3%
5 4101
 
2.3%
6 3878
 
2.2%
7 3728
 
2.1%
Other values (5) 3577
 
2.0%
Distinct11076
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:12.277407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.992663406
Min length4

Characters and Unicode

Total characters179788
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6441 ?
Unique (%)35.8%

Sample

1st row1974-05-09
2nd row1979-11-11
3rd row1976-01-28
4th row1976-03-19
5th row1995-07-31
ValueCountFrequency (%)
none 22
 
0.1%
1985-11-28 8
 
< 0.1%
1992-09-30 8
 
< 0.1%
1993-02-16 7
 
< 0.1%
1985-01-16 7
 
< 0.1%
1975-07-03 7
 
< 0.1%
1991-01-04 7
 
< 0.1%
1981-11-05 7
 
< 0.1%
1988-06-01 6
 
< 0.1%
1990-11-24 6
 
< 0.1%
Other values (11066) 17907
99.5%
2024-01-27T14:09:12.598860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 35940
20.0%
1 35332
19.7%
9 26172
14.6%
0 24460
13.6%
2 12759
 
7.1%
8 10032
 
5.6%
7 9913
 
5.5%
6 7810
 
4.3%
5 6315
 
3.5%
3 5843
 
3.2%
Other values (5) 5212
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143760
80.0%
Dash Punctuation 35940
 
20.0%
Lowercase Letter 66
 
< 0.1%
Uppercase Letter 22
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35332
24.6%
9 26172
18.2%
0 24460
17.0%
2 12759
 
8.9%
8 10032
 
7.0%
7 9913
 
6.9%
6 7810
 
5.4%
5 6315
 
4.4%
3 5843
 
4.1%
4 5124
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
o 22
33.3%
n 22
33.3%
e 22
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 35940
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 179700
> 99.9%
Latin 88
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 35940
20.0%
1 35332
19.7%
9 26172
14.6%
0 24460
13.6%
2 12759
 
7.1%
8 10032
 
5.6%
7 9913
 
5.5%
6 7810
 
4.3%
5 6315
 
3.5%
3 5843
 
3.3%
Latin
ValueCountFrequency (%)
N 22
25.0%
o 22
25.0%
n 22
25.0%
e 22
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 35940
20.0%
1 35332
19.7%
9 26172
14.6%
0 24460
13.6%
2 12759
 
7.1%
8 10032
 
5.6%
7 9913
 
5.5%
6 7810
 
4.3%
5 6315
 
3.5%
3 5843
 
3.2%
Other values (5) 5212
 
2.9%
Distinct2
Distinct (%)< 0.1%
Missing23
Missing (%)0.1%
Memory size281.1 KiB
2024-01-27T14:09:12.719357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.912237743
Min length4

Characters and Unicode

Total characters88268
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale
ValueCountFrequency (%)
male 9773
54.4%
female 8196
45.6%
2024-01-27T14:09:12.938692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 26165
29.6%
a 17969
20.4%
l 17969
20.4%
M 9773
 
11.1%
F 8196
 
9.3%
m 8196
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70299
79.6%
Uppercase Letter 17969
 
20.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26165
37.2%
a 17969
25.6%
l 17969
25.6%
m 8196
 
11.7%
Uppercase Letter
ValueCountFrequency (%)
M 9773
54.4%
F 8196
45.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 88268
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26165
29.6%
a 17969
20.4%
l 17969
20.4%
M 9773
 
11.1%
F 8196
 
9.3%
m 8196
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26165
29.6%
a 17969
20.4%
l 17969
20.4%
M 9773
 
11.1%
F 8196
 
9.3%
m 8196
 
9.3%

flg_substandard
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.04028743079
Minimum0
Maximum1
Zeros16294
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:13.051694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1966383258
Coefficient of variation (CV)4.880885227
Kurtosis19.8698206
Mean0.04028743079
Median Absolute Deviation (MAD)0
Skewness4.676267738
Sum684
Variance0.03866663117
MonotonicityNot monotonic
2024-01-27T14:09:13.148756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16294
90.6%
1 684
 
3.8%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16294
90.6%
1 684
 
3.8%
ValueCountFrequency (%)
1 684
 
3.8%
0 16294
90.6%

flg_is_borderline_standard
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.03310166097
Minimum0
Maximum1
Zeros16416
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:13.261454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1789073119
Coefficient of variation (CV)5.404783527
Kurtosis25.25198875
Mean0.03310166097
Median Absolute Deviation (MAD)0
Skewness5.220058821
Sum562
Variance0.03200782627
MonotonicityNot monotonic
2024-01-27T14:09:13.358446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16416
91.2%
1 562
 
3.1%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16416
91.2%
1 562
 
3.1%
ValueCountFrequency (%)
1 562
 
3.1%
0 16416
91.2%

flg_is_revised_term
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.0007067970315
Minimum0
Maximum1
Zeros16966
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:13.454823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02657704033
Coefficient of variation (CV)37.60208256
Kurtosis1410.249677
Mean0.0007067970315
Median Absolute Deviation (MAD)0
Skewness37.57770017
Sum12
Variance0.0007063390726
MonotonicityNot monotonic
2024-01-27T14:09:13.551335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16966
94.3%
1 12
 
0.1%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16966
94.3%
1 12
 
0.1%
ValueCountFrequency (%)
1 12
 
0.1%
0 16966
94.3%

flg_is_rental_flat
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.01254564731
Minimum0
Maximum1
Zeros16765
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:13.672127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1113058118
Coefficient of variation (CV)8.872066073
Kurtosis74.7439888
Mean0.01254564731
Median Absolute Deviation (MAD)0
Skewness8.7598621
Sum213
Variance0.01238898375
MonotonicityNot monotonic
2024-01-27T14:09:13.769506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16765
93.2%
1 213
 
1.2%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16765
93.2%
1 213
 
1.2%
ValueCountFrequency (%)
1 213
 
1.2%
0 16765
93.2%

flg_has_health_claim
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.04876899517
Minimum0
Maximum1
Zeros16150
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:13.873786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2153910695
Coefficient of variation (CV)4.416557462
Kurtosis15.56103597
Mean0.04876899517
Median Absolute Deviation (MAD)0
Skewness4.190370259
Sum828
Variance0.04639331283
MonotonicityNot monotonic
2024-01-27T14:09:13.962732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16150
89.8%
1 828
 
4.6%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16150
89.8%
1 828
 
4.6%
ValueCountFrequency (%)
1 828
 
4.6%
0 16150
89.8%

flg_has_life_claim
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.00359288491
Minimum0
Maximum1
Zeros16917
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:14.058960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05983466353
Coefficient of variation (CV)16.65365438
Kurtosis273.412341
Mean0.00359288491
Median Absolute Deviation (MAD)0
Skewness16.59458144
Sum61
Variance0.00358018696
MonotonicityNot monotonic
2024-01-27T14:09:14.155583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16917
94.0%
1 61
 
0.3%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16917
94.0%
1 61
 
0.3%
ValueCountFrequency (%)
1 61
 
0.3%
0 16917
94.0%

flg_gi_claim
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.03327836023
Minimum0
Maximum1
Zeros16413
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:14.268232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1793677952
Coefficient of variation (CV)5.389922878
Kurtosis25.0917237
Mean0.03327836023
Median Absolute Deviation (MAD)0
Skewness5.204687111
Sum565
Variance0.03217280594
MonotonicityNot monotonic
2024-01-27T14:09:14.374672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16413
91.2%
1 565
 
3.1%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16413
91.2%
1 565
 
3.1%
ValueCountFrequency (%)
1 565
 
3.1%
0 16413
91.2%

flg_is_proposal
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.002886087878
Minimum0
Maximum1
Zeros16929
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:14.468893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05364632219
Coefficient of variation (CV)18.58790323
Kurtosis341.5936343
Mean0.002886087878
Median Absolute Deviation (MAD)0
Skewness18.53519341
Sum49
Variance0.002877927884
MonotonicityNot monotonic
2024-01-27T14:09:14.573064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16929
94.1%
1 49
 
0.3%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16929
94.1%
1 49
 
0.3%
ValueCountFrequency (%)
1 49
 
0.3%
0 16929
94.1%

flg_with_preauthorisation
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.008245965367
Minimum0
Maximum1
Zeros16838
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:14.677406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09043478938
Coefficient of variation (CV)10.9671561
Kurtosis116.3143481
Mean0.008245965367
Median Absolute Deviation (MAD)0
Skewness10.87661006
Sum140
Variance0.008178451131
MonotonicityNot monotonic
2024-01-27T14:09:14.774772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16838
93.6%
1 140
 
0.8%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16838
93.6%
1 140
 
0.8%
ValueCountFrequency (%)
1 140
 
0.8%
0 16838
93.6%

flg_is_returned_mail
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.01926021911
Minimum0
Maximum1
Zeros16651
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:14.887669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1374422632
Coefficient of variation (CV)7.136069554
Kurtosis46.95430798
Mean0.01926021911
Median Absolute Deviation (MAD)0
Skewness6.996340243
Sum327
Variance0.01889037571
MonotonicityNot monotonic
2024-01-27T14:09:14.991815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16651
92.5%
1 327
 
1.8%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16651
92.5%
1 327
 
1.8%
ValueCountFrequency (%)
1 327
 
1.8%
0 16651
92.5%

is_consent_to_mail
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.3217693486
Minimum0
Maximum1
Zeros11515
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:15.088630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.467168802
Coefficient of variation (CV)1.45187478
Kurtosis-1.417823253
Mean0.3217693486
Median Absolute Deviation (MAD)0
Skewness0.7631145257
Sum5463
Variance0.2182466896
MonotonicityNot monotonic
2024-01-27T14:09:15.185077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 11515
64.0%
1 5463
30.4%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 11515
64.0%
1 5463
30.4%
ValueCountFrequency (%)
1 5463
30.4%
0 11515
64.0%

is_consent_to_email
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.3829661915
Minimum0
Maximum1
Zeros10476
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:15.281453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4861244765
Coefficient of variation (CV)1.269366558
Kurtosis-1.768314052
Mean0.3829661915
Median Absolute Deviation (MAD)0
Skewness0.4815540145
Sum6502
Variance0.2363170067
MonotonicityNot monotonic
2024-01-27T14:09:15.377841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 10476
58.2%
1 6502
36.1%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 10476
58.2%
1 6502
36.1%
ValueCountFrequency (%)
1 6502
36.1%
0 10476
58.2%

is_consent_to_call
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.1191541996
Minimum0
Maximum1
Zeros14955
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:15.474042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3239794107
Coefficient of variation (CV)2.7189928
Kurtosis3.529151513
Mean0.1191541996
Median Absolute Deviation (MAD)0
Skewness2.351326391
Sum2023
Variance0.1049626586
MonotonicityNot monotonic
2024-01-27T14:09:15.573811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14955
83.1%
1 2023
 
11.2%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 14955
83.1%
1 2023
 
11.2%
ValueCountFrequency (%)
1 2023
 
11.2%
0 14955
83.1%

is_consent_to_sms
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.2540346331
Minimum0
Maximum1
Zeros12665
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:15.666735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4353299903
Coefficient of variation (CV)1.713663941
Kurtosis-0.722843539
Mean0.2540346331
Median Absolute Deviation (MAD)0
Skewness1.130151152
Sum4313
Variance0.1895122005
MonotonicityNot monotonic
2024-01-27T14:09:15.762601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 12665
70.4%
1 4313
 
24.0%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 12665
70.4%
1 4313
 
24.0%
ValueCountFrequency (%)
1 4313
 
24.0%
0 12665
70.4%

is_valid_dm
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.713806102
Minimum0
Maximum1
Zeros4859
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:15.867875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4519944512
Coefficient of variation (CV)0.63321741
Kurtosis-1.104896719
Mean0.713806102
Median Absolute Deviation (MAD)0
Skewness-0.9461677712
Sum12119
Variance0.2042989839
MonotonicityNot monotonic
2024-01-27T14:09:15.997076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 12119
67.4%
0 4859
27.0%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 4859
27.0%
1 12119
67.4%
ValueCountFrequency (%)
1 12119
67.4%
0 4859
27.0%

is_valid_email
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.7434326776
Minimum0
Maximum1
Zeros4356
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:16.085796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4367513786
Coefficient of variation (CV)0.5874793936
Kurtosis-0.7571453841
Mean0.7434326776
Median Absolute Deviation (MAD)0
Skewness-1.114873904
Sum12622
Variance0.1907517667
MonotonicityNot monotonic
2024-01-27T14:09:16.189942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 12622
70.2%
0 4356
 
24.2%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 4356
 
24.2%
1 12622
70.2%
ValueCountFrequency (%)
1 12622
70.2%
0 4356
 
24.2%

is_housewife_retiree
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.003180586642
Minimum0
Maximum1
Zeros16924
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:16.295773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05630858959
Coefficient of variation (CV)17.70383767
Kurtosis309.502092
Mean0.003180586642
Median Absolute Deviation (MAD)0
Skewness17.64838896
Sum54
Variance0.003170657261
MonotonicityNot monotonic
2024-01-27T14:09:16.391264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16924
94.1%
1 54
 
0.3%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16924
94.1%
1 54
 
0.3%
ValueCountFrequency (%)
1 54
 
0.3%
0 16924
94.1%

is_sg_pr
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.9184238426
Minimum0
Maximum1
Zeros1385
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:16.495471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2737259598
Coefficient of variation (CV)0.2980388216
Kurtosis7.349823375
Mean0.9184238426
Median Absolute Deviation (MAD)0
Skewness-3.057606512
Sum15593
Variance0.07492590104
MonotonicityNot monotonic
2024-01-27T14:09:16.592417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 15593
86.7%
0 1385
 
7.7%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 1385
 
7.7%
1 15593
86.7%
ValueCountFrequency (%)
1 15593
86.7%
0 1385
 
7.7%

is_class_1_2
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.533101661
Minimum0
Maximum1
Zeros7927
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:16.680783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4989177701
Coefficient of variation (CV)0.9358773506
Kurtosis-1.982621758
Mean0.533101661
Median Absolute Deviation (MAD)0
Skewness-0.1327094875
Sum9051
Variance0.2489189413
MonotonicityNot monotonic
2024-01-27T14:09:16.785449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 9051
50.3%
0 7927
44.1%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 7927
44.1%
1 9051
50.3%
ValueCountFrequency (%)
1 9051
50.3%
0 7927
44.1%

is_dependent_in_at_least_1_policy
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing1014
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros16978
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:16.882721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:16.979084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 16978
94.4%
(Missing) 1014
 
5.6%
ValueCountFrequency (%)
0 16978
94.4%
ValueCountFrequency (%)
0 16978
94.4%

f_ever_declined_la
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing16759
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:17.075504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum1233
Variance0
MonotonicityIncreasing
2024-01-27T14:09:17.164664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 1233
 
6.9%
(Missing) 16759
93.1%
ValueCountFrequency (%)
1 1233
6.9%
ValueCountFrequency (%)
1 1233
6.9%

hh_20
Text

MISSING 

Distinct337
Distinct (%)2.2%
Missing2809
Missing (%)15.6%
Memory size281.1 KiB
2024-01-27T14:09:17.357402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.276822762
Min length1

Characters and Unicode

Total characters34569
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st row144
2nd row153
3rd row62
4th row1
5th row114
ValueCountFrequency (%)
1 1353
 
8.9%
90 207
 
1.4%
84 180
 
1.2%
82 171
 
1.1%
111 165
 
1.1%
85 163
 
1.1%
91 163
 
1.1%
89 163
 
1.1%
108 161
 
1.1%
110 160
 
1.1%
Other values (327) 12297
81.0%
2024-01-27T14:09:17.727486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9544
27.6%
2 3571
 
10.3%
8 3110
 
9.0%
0 2885
 
8.3%
9 2849
 
8.2%
7 2599
 
7.5%
6 2595
 
7.5%
5 2592
 
7.5%
4 2419
 
7.0%
3 2405
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34569
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9544
27.6%
2 3571
 
10.3%
8 3110
 
9.0%
0 2885
 
8.3%
9 2849
 
8.2%
7 2599
 
7.5%
6 2595
 
7.5%
5 2592
 
7.5%
4 2419
 
7.0%
3 2405
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34569
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9544
27.6%
2 3571
 
10.3%
8 3110
 
9.0%
0 2885
 
8.3%
9 2849
 
8.2%
7 2599
 
7.5%
6 2595
 
7.5%
5 2592
 
7.5%
4 2419
 
7.0%
3 2405
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9544
27.6%
2 3571
 
10.3%
8 3110
 
9.0%
0 2885
 
8.3%
9 2849
 
8.2%
7 2599
 
7.5%
6 2595
 
7.5%
5 2592
 
7.5%
4 2419
 
7.0%
3 2405
 
7.0%

pop_20
Text

MISSING 

Distinct869
Distinct (%)5.7%
Missing2809
Missing (%)15.6%
Memory size281.1 KiB
2024-01-27T14:09:17.952428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.693011921
Min length1

Characters and Unicode

Total characters40888
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)0.4%

Sample

1st row202
2nd row480
3rd row179
4th row4
5th row478
ValueCountFrequency (%)
3 567
 
3.7%
2 294
 
1.9%
4 254
 
1.7%
1 195
 
1.3%
0 77
 
0.5%
248 58
 
0.4%
397 50
 
0.3%
5 50
 
0.3%
263 48
 
0.3%
401 48
 
0.3%
Other values (859) 13542
89.2%
2024-01-27T14:09:18.307079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 6449
15.8%
2 5808
14.2%
4 5267
12.9%
1 4904
12.0%
5 3756
9.2%
6 3247
7.9%
8 3003
7.3%
7 2880
7.0%
9 2808
6.9%
0 2766
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40888
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6449
15.8%
2 5808
14.2%
4 5267
12.9%
1 4904
12.0%
5 3756
9.2%
6 3247
7.9%
8 3003
7.3%
7 2880
7.0%
9 2808
6.9%
0 2766
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 40888
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 6449
15.8%
2 5808
14.2%
4 5267
12.9%
1 4904
12.0%
5 3756
9.2%
6 3247
7.9%
8 3003
7.3%
7 2880
7.0%
9 2808
6.9%
0 2766
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 6449
15.8%
2 5808
14.2%
4 5267
12.9%
1 4904
12.0%
5 3756
9.2%
6 3247
7.9%
8 3003
7.3%
7 2880
7.0%
9 2808
6.9%
0 2766
6.8%

hh_size
Real number (ℝ)

MISSING 

Distinct4934
Distinct (%)32.5%
Missing2809
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean3.235774808
Minimum0
Maximum8
Zeros77
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:18.450913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.337238372
Q12.635361552
median3.25
Q33.847963676
95-th percentile4.91013986
Maximum8
Range8
Interquartile range (IQR)1.212602124

Descriptive statistics

Standard deviation1.130879803
Coefficient of variation (CV)0.3494927397
Kurtosis3.310203681
Mean3.235774808
Median Absolute Deviation (MAD)0.6029411765
Skewness0.7416608544
Sum49128.76891
Variance1.278889128
MonotonicityNot monotonic
2024-01-27T14:09:18.603676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 676
 
3.8%
1 502
 
2.8%
2 341
 
1.9%
4 287
 
1.6%
8 78
 
0.4%
0 77
 
0.4%
3.5 65
 
0.4%
5 61
 
0.3%
2.5 56
 
0.3%
3.666666667 45
 
0.3%
Other values (4924) 12995
72.2%
(Missing) 2809
 
15.6%
ValueCountFrequency (%)
0 77
 
0.4%
1 502
2.8%
1.013157895 1
 
< 0.1%
1.024390244 3
 
< 0.1%
1.025 1
 
< 0.1%
ValueCountFrequency (%)
8 78
0.4%
7.99137931 5
 
< 0.1%
7.989690722 1
 
< 0.1%
7.988888889 1
 
< 0.1%
7.988636364 3
 
< 0.1%

hh_size_est
Text

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing2809
Missing (%)15.6%
Memory size281.1 KiB
2024-01-27T14:09:18.701128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.17618389
Min length1

Characters and Unicode

Total characters17858
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row4
5th row>4
ValueCountFrequency (%)
3 6096
40.2%
4 5897
38.8%
2 2276
 
15.0%
1 837
 
5.5%
0 77
 
0.5%
2024-01-27T14:09:18.894058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 6096
34.1%
4 5897
33.0%
> 2675
15.0%
2 2276
 
12.7%
1 837
 
4.7%
0 77
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15183
85.0%
Math Symbol 2675
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6096
40.2%
4 5897
38.8%
2 2276
 
15.0%
1 837
 
5.5%
0 77
 
0.5%
Math Symbol
ValueCountFrequency (%)
> 2675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 6096
34.1%
4 5897
33.0%
> 2675
15.0%
2 2276
 
12.7%
1 837
 
4.7%
0 77
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 6096
34.1%
4 5897
33.0%
> 2675
15.0%
2 2276
 
12.7%
1 837
 
4.7%
0 77
 
0.4%

annual_income_est
Text

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2809
Missing (%)15.6%
Memory size281.1 KiB
2024-01-27T14:09:18.990742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.06000132
Min length9

Characters and Unicode

Total characters152741
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC.60K-100K
2nd rowD.30K-60K
3rd rowA.ABOVE200K
4th rowB.100K-200K
5th rowE.BELOW30K
ValueCountFrequency (%)
e.below30k 7771
51.2%
c.60k-100k 2679
 
17.6%
a.above200k 2097
 
13.8%
d.30k-60k 1911
 
12.6%
b.100k-200k 725
 
4.8%
2024-01-27T14:09:19.223777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26724
17.5%
K 20498
13.4%
E 17639
11.5%
. 15183
9.9%
B 10593
 
6.9%
O 9868
 
6.5%
3 9682
 
6.3%
L 7771
 
5.1%
W 7771
 
5.1%
- 5315
 
3.5%
Other values (7) 21697
14.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 85021
55.7%
Decimal Number 47222
30.9%
Other Punctuation 15183
 
9.9%
Dash Punctuation 5315
 
3.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 20498
24.1%
E 17639
20.7%
B 10593
12.5%
O 9868
11.6%
L 7771
 
9.1%
W 7771
 
9.1%
A 4194
 
4.9%
C 2679
 
3.2%
V 2097
 
2.5%
D 1911
 
2.2%
Decimal Number
ValueCountFrequency (%)
0 26724
56.6%
3 9682
 
20.5%
6 4590
 
9.7%
1 3404
 
7.2%
2 2822
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 15183
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5315
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85021
55.7%
Common 67720
44.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 20498
24.1%
E 17639
20.7%
B 10593
12.5%
O 9868
11.6%
L 7771
 
9.1%
W 7771
 
9.1%
A 4194
 
4.9%
C 2679
 
3.2%
V 2097
 
2.5%
D 1911
 
2.2%
Common
ValueCountFrequency (%)
0 26724
39.5%
. 15183
22.4%
3 9682
 
14.3%
- 5315
 
7.8%
6 4590
 
6.8%
1 3404
 
5.0%
2 2822
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26724
17.5%
K 20498
13.4%
E 17639
11.5%
. 15183
9.9%
B 10593
 
6.9%
O 9868
 
6.5%
3 9682
 
6.3%
L 7771
 
5.1%
W 7771
 
5.1%
- 5315
 
3.5%
Other values (7) 21697
14.2%
Distinct395
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.21770787
Minimum-4
Maximum847
Zeros25
Zeros (%)0.1%
Negative8
Negative (%)< 0.1%
Memory size281.1 KiB
2024-01-27T14:09:19.359398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile2
Q18
median31
Q375
95-th percentile172
Maximum847
Range851
Interquartile range (IQR)67

Descriptive statistics

Standard deviation62.54546598
Coefficient of variation (CV)1.197782678
Kurtosis13.93975625
Mean52.21770787
Median Absolute Deviation (MAD)23
Skewness2.791380134
Sum939501
Variance3911.935314
MonotonicityNot monotonic
2024-01-27T14:09:19.488534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 2015
 
11.2%
13 551
 
3.1%
5 502
 
2.8%
2 496
 
2.8%
1 412
 
2.3%
4 360
 
2.0%
11 322
 
1.8%
14 317
 
1.8%
3 313
 
1.7%
6 302
 
1.7%
Other values (385) 12402
68.9%
ValueCountFrequency (%)
-4 2
 
< 0.1%
-3 1
 
< 0.1%
-2 2
 
< 0.1%
-1 3
 
< 0.1%
0 25
0.1%
ValueCountFrequency (%)
847 1
< 0.1%
785 1
< 0.1%
748 1
< 0.1%
669 1
< 0.1%
638 1
< 0.1%

flg_latest_being_lapse
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09637616719
Minimum0
Maximum1
Zeros16258
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:19.593511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2951146256
Coefficient of variation (CV)3.062112078
Kurtosis5.484521912
Mean0.09637616719
Median Absolute Deviation (MAD)0
Skewness2.735674005
Sum1734
Variance0.08709264222
MonotonicityNot monotonic
2024-01-27T14:09:19.706337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16258
90.4%
1 1734
 
9.6%
ValueCountFrequency (%)
0 16258
90.4%
1 1734
 
9.6%
ValueCountFrequency (%)
1 1734
 
9.6%
0 16258
90.4%

flg_latest_being_cancel
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007281013784
Minimum0
Maximum1
Zeros17861
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:19.795771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08502001164
Coefficient of variation (CV)11.67694694
Kurtosis132.3879673
Mean0.007281013784
Median Absolute Deviation (MAD)0
Skewness11.59194768
Sum131
Variance0.007228402379
MonotonicityNot monotonic
2024-01-27T14:09:19.900981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17861
99.3%
1 131
 
0.7%
ValueCountFrequency (%)
0 17861
99.3%
1 131
 
0.7%
ValueCountFrequency (%)
1 131
 
0.7%
0 17861
99.3%

recency_lapse
Real number (ℝ)

MISSING 

Distinct304
Distinct (%)5.6%
Missing12592
Missing (%)70.0%
Infinite0
Infinite (%)0.0%
Mean76.99314815
Minimum1
Maximum489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:20.184632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q132
median63
Q3104
95-th percentile209
Maximum489
Range488
Interquartile range (IQR)72

Descriptive statistics

Standard deviation62.93023321
Coefficient of variation (CV)0.8173484878
Kurtosis3.240919585
Mean76.99314815
Median Absolute Deviation (MAD)35
Skewness1.438229801
Sum415763
Variance3960.214252
MonotonicityNot monotonic
2024-01-27T14:09:20.321734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 118
 
0.7%
50 115
 
0.6%
3 108
 
0.6%
4 94
 
0.5%
38 94
 
0.5%
32 94
 
0.5%
9 83
 
0.5%
5 78
 
0.4%
68 75
 
0.4%
81 72
 
0.4%
Other values (294) 4469
 
24.8%
(Missing) 12592
70.0%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 48
0.3%
3 108
0.6%
4 94
0.5%
5 78
0.4%
ValueCountFrequency (%)
489 1
< 0.1%
487 1
< 0.1%
486 1
< 0.1%
452 1
< 0.1%
432 1
< 0.1%

recency_cancel
Real number (ℝ)

MISSING 

Distinct112
Distinct (%)17.9%
Missing17368
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean47.96474359
Minimum1
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:20.458573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median47
Q373
95-th percentile95
Maximum147
Range146
Interquartile range (IQR)57

Descriptive statistics

Standard deviation31.41027693
Coefficient of variation (CV)0.6548617709
Kurtosis-0.6826401818
Mean47.96474359
Median Absolute Deviation (MAD)27
Skewness0.3055474401
Sum29930
Variance986.6054966
MonotonicityNot monotonic
2024-01-27T14:09:20.587523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 41
 
0.2%
13 16
 
0.1%
9 16
 
0.1%
41 15
 
0.1%
3 12
 
0.1%
60 10
 
0.1%
94 10
 
0.1%
72 10
 
0.1%
73 10
 
0.1%
50 9
 
0.1%
Other values (102) 475
 
2.6%
(Missing) 17368
96.5%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 6
< 0.1%
3 12
0.1%
4 4
 
< 0.1%
5 8
< 0.1%
ValueCountFrequency (%)
147 1
< 0.1%
145 1
< 0.1%
142 1
< 0.1%
135 1
< 0.1%
134 1
< 0.1%

tot_inforce_pols
Real number (ℝ)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.50655847
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:20.708366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum54
Range53
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.573981509
Coefficient of variation (CV)1.02689865
Kurtosis23.4853292
Mean2.50655847
Median Absolute Deviation (MAD)1
Skewness3.611747212
Sum45098
Variance6.625380807
MonotonicityNot monotonic
2024-01-27T14:09:20.837242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 8686
48.3%
2 3746
20.8%
3 2074
 
11.5%
4 1178
 
6.5%
5 704
 
3.9%
6 470
 
2.6%
7 252
 
1.4%
8 237
 
1.3%
9 151
 
0.8%
10 112
 
0.6%
Other values (19) 382
 
2.1%
ValueCountFrequency (%)
1 8686
48.3%
2 3746
20.8%
3 2074
 
11.5%
4 1178
 
6.5%
5 704
 
3.9%
ValueCountFrequency (%)
54 1
< 0.1%
31 1
< 0.1%
29 2
< 0.1%
27 1
< 0.1%
26 1
< 0.1%

tot_cancel_pols
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)0.8%
Missing17368
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean1.217948718
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:20.933542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5920269808
Coefficient of variation (CV)0.4860853106
Kurtosis18.29108035
Mean1.217948718
Median Absolute Deviation (MAD)0
Skewness3.715399393
Sum760
Variance0.350495946
MonotonicityNot monotonic
2024-01-27T14:09:21.030145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1 526
 
2.9%
2 72
 
0.4%
3 18
 
0.1%
4 6
 
< 0.1%
6 2
 
< 0.1%
(Missing) 17368
96.5%
ValueCountFrequency (%)
1 526
2.9%
2 72
 
0.4%
3 18
 
0.1%
4 6
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
4 6
 
< 0.1%
3 18
 
0.1%
2 72
 
0.4%
1 526
2.9%

ape_gi_42e115
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.134190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_ltc_1280bf
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.214529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_grp_6fc3e6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_de05ae
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_inv_dcd836
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.295121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_grp_945b5a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_6a5788
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_ltc_43b9d5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_9cdedf
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_lh_d0adeb
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.375329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_grp_1581d7
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_22decf
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_lh_507c37
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_lh_839f8a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_inv_e9f316
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_gi_a10d1b
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.463701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_gi_29d435
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.544432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_grp_caa6ff
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_fd3bfb
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_lh_e22a6a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_70e1dd
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_e04c3a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_grp_fe5fb8
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_gi_856320
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.617017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_grp_94baec
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_gi_058815
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.697153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_grp_e91421
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_lh_f852af
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_lh_947b15
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_32c74c
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.789352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_gi_42e115
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.868006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_ltc_1280bf
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:21.948445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_6fc3e6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_grp_de05ae
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_inv_dcd836
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.029844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_945b5a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_grp_6a5788
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_ltc_43b9d5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_grp_9cdedf
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_lh_d0adeb
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.109131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_1581d7
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_grp_22decf
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.197407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_lh_507c37
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_inv_e9f316
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_gi_a10d1b
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.278264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_gi_29d435
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.359798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_caa6ff
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_grp_fd3bfb
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_lh_e22a6a
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.448547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_70e1dd
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_grp_e04c3a
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.527781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_fe5fb8
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_gi_856320
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.617215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_94baec
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.697504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_gi_058815
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.785605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_grp_e91421
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_lh_f852af
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_lh_947b15
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_32c74c
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.865838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_gi_42e115
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:22.939427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_ltc_1280bf
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.026689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_grp_6fc3e6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_de05ae
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_inv_dcd836
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.107912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_grp_945b5a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_6a5788
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_ltc_43b9d5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_9cdedf
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_lh_d0adeb
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.188365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_grp_1581d7
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_22decf
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_lh_507c37
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_lh_839f8a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_inv_e9f316
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_gi_a10d1b
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.269244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_gi_29d435
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.350066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_grp_caa6ff
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_fd3bfb
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_lh_e22a6a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_70e1dd
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_e04c3a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_grp_fe5fb8
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_gi_856320
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.431031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_grp_94baec
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_gi_058815
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.511334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_grp_e91421
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_lh_f852af
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_lh_947b15
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_32c74c
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.583981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_839f8a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_e22a6a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_d0adeb
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.673547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ape_c4bda5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_ltc
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_507c37
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

ape_gi
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:23.754261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

f_hold_839f8a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1083259226
Minimum0
Maximum1
Zeros16043
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:23.835520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3108002349
Coefficient of variation (CV)2.869121511
Kurtosis4.354430195
Mean0.1083259226
Median Absolute Deviation (MAD)0
Skewness2.520703506
Sum1949
Variance0.09659678599
MonotonicityNot monotonic
2024-01-27T14:09:23.931819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16043
89.2%
1 1949
 
10.8%
ValueCountFrequency (%)
0 16043
89.2%
1 1949
 
10.8%
ValueCountFrequency (%)
1 1949
 
10.8%
0 16043
89.2%

f_hold_e22a6a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2058692752
Minimum0
Maximum1
Zeros14288
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:24.029098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4043466383
Coefficient of variation (CV)1.964094146
Kurtosis0.1170559162
Mean0.2058692752
Median Absolute Deviation (MAD)0
Skewness1.455006157
Sum3704
Variance0.1634962039
MonotonicityNot monotonic
2024-01-27T14:09:24.125719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14288
79.4%
1 3704
 
20.6%
ValueCountFrequency (%)
0 14288
79.4%
1 3704
 
20.6%
ValueCountFrequency (%)
1 3704
 
20.6%
0 14288
79.4%

f_hold_d0adeb
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:24.213422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:24.310040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%

f_hold_c4bda5
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004335260116
Minimum0
Maximum1
Zeros17914
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:24.397460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06570164046
Coefficient of variation (CV)15.1551784
Kurtosis225.7340812
Mean0.004335260116
Median Absolute Deviation (MAD)0
Skewness15.09002944
Sum78
Variance0.004316705559
MonotonicityNot monotonic
2024-01-27T14:09:24.502315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17914
99.6%
1 78
 
0.4%
ValueCountFrequency (%)
0 17914
99.6%
1 78
 
0.4%
ValueCountFrequency (%)
1 78
 
0.4%
0 17914
99.6%

f_hold_ltc
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1861382837
Minimum0
Maximum1
Zeros14643
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:24.597775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3892290372
Coefficient of variation (CV)2.091074601
Kurtosis0.6015605435
Mean0.1861382837
Median Absolute Deviation (MAD)0
Skewness1.612914656
Sum3349
Variance0.1514992434
MonotonicityNot monotonic
2024-01-27T14:09:24.695020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14643
81.4%
1 3349
 
18.6%
ValueCountFrequency (%)
0 14643
81.4%
1 3349
 
18.6%
ValueCountFrequency (%)
1 3349
 
18.6%
0 14643
81.4%

f_hold_507c37
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3428190307
Minimum0
Maximum1
Zeros11824
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:24.791146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4746647928
Coefficient of variation (CV)1.384592891
Kurtosis-1.561458634
Mean0.3428190307
Median Absolute Deviation (MAD)0
Skewness0.6623556075
Sum6168
Variance0.2253066655
MonotonicityNot monotonic
2024-01-27T14:09:24.887275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 11824
65.7%
1 6168
34.3%
ValueCountFrequency (%)
0 11824
65.7%
1 6168
34.3%
ValueCountFrequency (%)
1 6168
34.3%
0 11824
65.7%

f_hold_gi
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:24.975732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:25.064336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%

sumins_839f8a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_e22a6a
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:25.161103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_d0adeb
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:25.242841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

sumins_c4bda5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_ltc
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_507c37
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

sumins_gi
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:25.339573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_839f8a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_e22a6a
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_d0adeb
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:25.420841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

prempaid_c4bda5
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_ltc
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_507c37
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size281.1 KiB

prempaid_gi
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
0.00
17992 
ValueCountFrequency (%)
0.00 17992
100.0%
2024-01-27T14:09:25.493318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

lapse_ape_ltc_1280bf
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
0.00
5400 
(Missing)
12592 
ValueCountFrequency (%)
0.00 5400
30.0%
(Missing) 12592
70.0%
2024-01-27T14:09:25.574055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

lapse_ape_grp_6fc3e6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_de05ae
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_inv_dcd836
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
0.00
5400 
(Missing)
12592 
ValueCountFrequency (%)
0.00 5400
30.0%
(Missing) 12592
70.0%
2024-01-27T14:09:25.643577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

lapse_ape_grp_945b5a
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_6a5788
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_ltc_43b9d5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_9cdedf
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_lh_d0adeb
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
0.00
5400 
(Missing)
12592 
ValueCountFrequency (%)
0.00 5400
30.0%
(Missing) 12592
70.0%
2024-01-27T14:09:25.711892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

lapse_ape_grp_1581d7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_22decf
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_lh_507c37
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_lh_839f8a
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_inv_e9f316
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_caa6ff
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_fd3bfb
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_lh_e22a6a
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_70e1dd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_e04c3a
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_fe5fb8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_94baec
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_grp_e91421
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_lh_f852af
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_lh_947b15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing12592
Missing (%)70.0%
Memory size281.1 KiB

lapse_ape_32c74c
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
0.00
5400 
(Missing)
12592 
ValueCountFrequency (%)
0.00 5400
30.0%
(Missing) 12592
70.0%
2024-01-27T14:09:25.784563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

n_months_since_lapse_ltc_1280bf
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:25.840904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21600
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5400
100.0%
2024-01-27T14:09:26.018264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21600
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21600
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21600
100.0%
Distinct29
Distinct (%)0.5%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:26.122587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.982777778
Min length1

Characters and Unicode

Total characters21507
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.4%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5350
99.1%
53 10
 
0.2%
83 5
 
0.1%
14 3
 
0.1%
50 3
 
0.1%
5 2
 
< 0.1%
19 2
 
< 0.1%
41 2
 
< 0.1%
26 2
 
< 0.1%
35 2
 
< 0.1%
Other values (19) 19
 
0.4%
2024-01-27T14:09:26.344211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21403
99.5%
3 22
 
0.1%
5 21
 
0.1%
1 14
 
0.1%
- 10
 
< 0.1%
8 9
 
< 0.1%
4 8
 
< 0.1%
7 7
 
< 0.1%
0 5
 
< 0.1%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21497
> 99.9%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21403
99.6%
3 22
 
0.1%
5 21
 
0.1%
1 14
 
0.1%
8 9
 
< 0.1%
4 8
 
< 0.1%
7 7
 
< 0.1%
0 5
 
< 0.1%
2 5
 
< 0.1%
6 3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21507
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21403
99.5%
3 22
 
0.1%
5 21
 
0.1%
1 14
 
0.1%
- 10
 
< 0.1%
8 9
 
< 0.1%
4 8
 
< 0.1%
7 7
 
< 0.1%
0 5
 
< 0.1%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21403
99.5%
3 22
 
0.1%
5 21
 
0.1%
1 14
 
0.1%
- 10
 
< 0.1%
8 9
 
< 0.1%
4 8
 
< 0.1%
7 7
 
< 0.1%
0 5
 
< 0.1%
2 5
 
< 0.1%
Distinct4
Distinct (%)0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:26.428395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.998518519
Min length2

Characters and Unicode

Total characters21592
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5394
99.9%
53 4
 
0.1%
34 1
 
< 0.1%
79 1
 
< 0.1%
2024-01-27T14:09:26.807239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21577
99.9%
3 5
 
< 0.1%
- 4
 
< 0.1%
5 4
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21588
> 99.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21577
99.9%
3 5
 
< 0.1%
5 4
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21577
99.9%
3 5
 
< 0.1%
- 4
 
< 0.1%
5 4
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21577
99.9%
3 5
 
< 0.1%
- 4
 
< 0.1%
5 4
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

n_months_since_lapse_inv_dcd836
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:26.897035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21600
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5400
100.0%
2024-01-27T14:09:27.090163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21600
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21600
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21600
100.0%
Distinct64
Distinct (%)1.2%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:27.210412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.929444444
Min length1

Characters and Unicode

Total characters21219
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5191
96.1%
53 53
 
1.0%
20 16
 
0.3%
8 11
 
0.2%
83 7
 
0.1%
26 6
 
0.1%
56 5
 
0.1%
50 5
 
0.1%
22 5
 
0.1%
10 4
 
0.1%
Other values (54) 97
 
1.8%
2024-01-27T14:09:27.446503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 20770
97.9%
3 90
 
0.4%
5 81
 
0.4%
2 57
 
0.3%
- 53
 
0.2%
8 35
 
0.2%
6 32
 
0.2%
1 31
 
0.1%
0 28
 
0.1%
4 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21166
99.8%
Dash Punctuation 53
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 20770
98.1%
3 90
 
0.4%
5 81
 
0.4%
2 57
 
0.3%
8 35
 
0.2%
6 32
 
0.2%
1 31
 
0.1%
0 28
 
0.1%
4 22
 
0.1%
7 20
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 20770
97.9%
3 90
 
0.4%
5 81
 
0.4%
2 57
 
0.3%
- 53
 
0.2%
8 35
 
0.2%
6 32
 
0.2%
1 31
 
0.1%
0 28
 
0.1%
4 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 20770
97.9%
3 90
 
0.4%
5 81
 
0.4%
2 57
 
0.3%
- 53
 
0.2%
8 35
 
0.2%
6 32
 
0.2%
1 31
 
0.1%
0 28
 
0.1%
4 22
 
0.1%
Distinct18
Distinct (%)0.3%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:27.541853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.987962963
Min length1

Characters and Unicode

Total characters21535
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5358
99.2%
53 18
 
0.3%
62 6
 
0.1%
20 3
 
0.1%
75 2
 
< 0.1%
115 1
 
< 0.1%
100 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
78 1
 
< 0.1%
Other values (8) 8
 
0.1%
2024-01-27T14:09:27.750376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21433
99.5%
5 22
 
0.1%
3 19
 
0.1%
- 18
 
0.1%
2 12
 
0.1%
6 8
 
< 0.1%
7 6
 
< 0.1%
0 5
 
< 0.1%
4 5
 
< 0.1%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21517
99.9%
Dash Punctuation 18
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21433
99.6%
5 22
 
0.1%
3 19
 
0.1%
2 12
 
0.1%
6 8
 
< 0.1%
7 6
 
< 0.1%
0 5
 
< 0.1%
4 5
 
< 0.1%
1 5
 
< 0.1%
8 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21433
99.5%
5 22
 
0.1%
3 19
 
0.1%
- 18
 
0.1%
2 12
 
0.1%
6 8
 
< 0.1%
7 6
 
< 0.1%
0 5
 
< 0.1%
4 5
 
< 0.1%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21433
99.5%
5 22
 
0.1%
3 19
 
0.1%
- 18
 
0.1%
2 12
 
0.1%
6 8
 
< 0.1%
7 6
 
< 0.1%
0 5
 
< 0.1%
4 5
 
< 0.1%
1 5
 
< 0.1%
Distinct22
Distinct (%)0.4%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:27.847181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.962592593
Min length1

Characters and Unicode

Total characters21398
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5322
98.6%
6 11
 
0.2%
3 9
 
0.2%
18 7
 
0.1%
2 6
 
0.1%
8 5
 
0.1%
7 5
 
0.1%
19 5
 
0.1%
12 4
 
0.1%
4 4
 
0.1%
Other values (12) 22
 
0.4%
2024-01-27T14:09:28.063602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21294
99.5%
1 35
 
0.2%
6 13
 
0.1%
2 13
 
0.1%
3 12
 
0.1%
8 12
 
0.1%
7 7
 
< 0.1%
4 6
 
< 0.1%
5 5
 
< 0.1%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21398
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21294
99.5%
1 35
 
0.2%
6 13
 
0.1%
2 13
 
0.1%
3 12
 
0.1%
8 12
 
0.1%
7 7
 
< 0.1%
4 6
 
< 0.1%
5 5
 
< 0.1%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21398
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21294
99.5%
1 35
 
0.2%
6 13
 
0.1%
2 13
 
0.1%
3 12
 
0.1%
8 12
 
0.1%
7 7
 
< 0.1%
4 6
 
< 0.1%
5 5
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21294
99.5%
1 35
 
0.2%
6 13
 
0.1%
2 13
 
0.1%
3 12
 
0.1%
8 12
 
0.1%
7 7
 
< 0.1%
4 6
 
< 0.1%
5 5
 
< 0.1%
0 1
 
< 0.1%
Distinct36
Distinct (%)0.7%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:28.168037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.974259259
Min length1

Characters and Unicode

Total characters21461
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.4%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5328
98.7%
53 12
 
0.2%
26 6
 
0.1%
83 5
 
0.1%
14 4
 
0.1%
38 3
 
0.1%
50 3
 
0.1%
8 3
 
0.1%
75 3
 
0.1%
5 2
 
< 0.1%
Other values (26) 31
 
0.6%
2024-01-27T14:09:28.400959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21316
99.3%
5 26
 
0.1%
3 26
 
0.1%
1 19
 
0.1%
8 13
 
0.1%
- 12
 
0.1%
2 11
 
0.1%
4 11
 
0.1%
7 10
 
< 0.1%
6 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21449
99.9%
Dash Punctuation 12
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21316
99.4%
5 26
 
0.1%
3 26
 
0.1%
1 19
 
0.1%
8 13
 
0.1%
2 11
 
0.1%
4 11
 
0.1%
7 10
 
< 0.1%
6 9
 
< 0.1%
0 8
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21461
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21316
99.3%
5 26
 
0.1%
3 26
 
0.1%
1 19
 
0.1%
8 13
 
0.1%
- 12
 
0.1%
2 11
 
0.1%
4 11
 
0.1%
7 10
 
< 0.1%
6 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21316
99.3%
5 26
 
0.1%
3 26
 
0.1%
1 19
 
0.1%
8 13
 
0.1%
- 12
 
0.1%
2 11
 
0.1%
4 11
 
0.1%
7 10
 
< 0.1%
6 9
 
< 0.1%

n_months_since_lapse_lh_d0adeb
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:28.481012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21600
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5400
100.0%
2024-01-27T14:09:28.667368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21600
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21600
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21600
100.0%
Distinct96
Distinct (%)1.8%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:28.814051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.804074074
Min length1

Characters and Unicode

Total characters20542
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.6%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 4866
90.1%
32 78
 
1.4%
78 71
 
1.3%
38 52
 
1.0%
31 34
 
0.6%
20 22
 
0.4%
53 18
 
0.3%
81 16
 
0.3%
56 11
 
0.2%
83 10
 
0.2%
Other values (85) 222
 
4.1%
2024-01-27T14:09:29.080673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 19499
94.9%
3 227
 
1.1%
8 178
 
0.9%
2 151
 
0.7%
1 121
 
0.6%
7 108
 
0.5%
6 68
 
0.3%
5 66
 
0.3%
0 55
 
0.3%
4 53
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20526
99.9%
Dash Punctuation 16
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 19499
95.0%
3 227
 
1.1%
8 178
 
0.9%
2 151
 
0.7%
1 121
 
0.6%
7 108
 
0.5%
6 68
 
0.3%
5 66
 
0.3%
0 55
 
0.3%
4 53
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 19499
94.9%
3 227
 
1.1%
8 178
 
0.9%
2 151
 
0.7%
1 121
 
0.6%
7 108
 
0.5%
6 68
 
0.3%
5 66
 
0.3%
0 55
 
0.3%
4 53
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 19499
94.9%
3 227
 
1.1%
8 178
 
0.9%
2 151
 
0.7%
1 121
 
0.6%
7 108
 
0.5%
6 68
 
0.3%
5 66
 
0.3%
0 55
 
0.3%
4 53
 
0.3%
Distinct48
Distinct (%)0.9%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:29.193925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.955185185
Min length1

Characters and Unicode

Total characters21358
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5281
97.8%
38 10
 
0.2%
8 10
 
0.2%
26 8
 
0.1%
20 8
 
0.1%
44 7
 
0.1%
28 6
 
0.1%
32 6
 
0.1%
68 5
 
0.1%
56 4
 
0.1%
Other values (38) 55
 
1.0%
2024-01-27T14:09:29.450898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21134
99.0%
2 40
 
0.2%
8 35
 
0.2%
3 29
 
0.1%
0 27
 
0.1%
1 27
 
0.1%
6 24
 
0.1%
4 21
 
0.1%
5 15
 
0.1%
7 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21358
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21134
99.0%
2 40
 
0.2%
8 35
 
0.2%
3 29
 
0.1%
0 27
 
0.1%
1 27
 
0.1%
6 24
 
0.1%
4 21
 
0.1%
5 15
 
0.1%
7 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21134
99.0%
2 40
 
0.2%
8 35
 
0.2%
3 29
 
0.1%
0 27
 
0.1%
1 27
 
0.1%
6 24
 
0.1%
4 21
 
0.1%
5 15
 
0.1%
7 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21134
99.0%
2 40
 
0.2%
8 35
 
0.2%
3 29
 
0.1%
0 27
 
0.1%
1 27
 
0.1%
6 24
 
0.1%
4 21
 
0.1%
5 15
 
0.1%
7 6
 
< 0.1%
Distinct114
Distinct (%)2.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:29.611905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.81962963
Min length1

Characters and Unicode

Total characters20626
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.3%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 4916
91.0%
48 13
 
0.2%
56 12
 
0.2%
74 12
 
0.2%
42 11
 
0.2%
65 11
 
0.2%
70 10
 
0.2%
52 10
 
0.2%
38 10
 
0.2%
45 10
 
0.2%
Other values (104) 385
 
7.1%
2024-01-27T14:09:29.958414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 19750
95.8%
5 131
 
0.6%
4 123
 
0.6%
7 102
 
0.5%
1 102
 
0.5%
6 97
 
0.5%
2 96
 
0.5%
3 86
 
0.4%
8 81
 
0.4%
0 58
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20626
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 19750
95.8%
5 131
 
0.6%
4 123
 
0.6%
7 102
 
0.5%
1 102
 
0.5%
6 97
 
0.5%
2 96
 
0.5%
3 86
 
0.4%
8 81
 
0.4%
0 58
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20626
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 19750
95.8%
5 131
 
0.6%
4 123
 
0.6%
7 102
 
0.5%
1 102
 
0.5%
6 97
 
0.5%
2 96
 
0.5%
3 86
 
0.4%
8 81
 
0.4%
0 58
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 19750
95.8%
5 131
 
0.6%
4 123
 
0.6%
7 102
 
0.5%
1 102
 
0.5%
6 97
 
0.5%
2 96
 
0.5%
3 86
 
0.4%
8 81
 
0.4%
0 58
 
0.3%
Distinct50
Distinct (%)0.9%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:30.103721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.956666667
Min length1

Characters and Unicode

Total characters21366
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5294
98.0%
26 15
 
0.3%
24 8
 
0.1%
2 5
 
0.1%
12 5
 
0.1%
15 5
 
0.1%
9 4
 
0.1%
7 4
 
0.1%
4 3
 
0.1%
35 3
 
0.1%
Other values (40) 54
 
1.0%
2024-01-27T14:09:30.376795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21185
99.2%
2 46
 
0.2%
1 28
 
0.1%
4 25
 
0.1%
3 23
 
0.1%
6 22
 
0.1%
5 16
 
0.1%
7 11
 
0.1%
8 6
 
< 0.1%
0 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21366
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21185
99.2%
2 46
 
0.2%
1 28
 
0.1%
4 25
 
0.1%
3 23
 
0.1%
6 22
 
0.1%
5 16
 
0.1%
7 11
 
0.1%
8 6
 
< 0.1%
0 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21366
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21185
99.2%
2 46
 
0.2%
1 28
 
0.1%
4 25
 
0.1%
3 23
 
0.1%
6 22
 
0.1%
5 16
 
0.1%
7 11
 
0.1%
8 6
 
< 0.1%
0 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21185
99.2%
2 46
 
0.2%
1 28
 
0.1%
4 25
 
0.1%
3 23
 
0.1%
6 22
 
0.1%
5 16
 
0.1%
7 11
 
0.1%
8 6
 
< 0.1%
0 4
 
< 0.1%
Distinct9
Distinct (%)0.2%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:30.472995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.992222222
Min length1

Characters and Unicode

Total characters21558
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5379
99.6%
26 13
 
0.2%
15 2
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
92 1
 
< 0.1%
25 1
 
< 0.1%
101 1
 
< 0.1%
11 1
 
< 0.1%
2024-01-27T14:09:30.705883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21517
99.8%
2 15
 
0.1%
6 14
 
0.1%
1 7
 
< 0.1%
5 3
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21558
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21517
99.8%
2 15
 
0.1%
6 14
 
0.1%
1 7
 
< 0.1%
5 3
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21558
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21517
99.8%
2 15
 
0.1%
6 14
 
0.1%
1 7
 
< 0.1%
5 3
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21517
99.8%
2 15
 
0.1%
6 14
 
0.1%
1 7
 
< 0.1%
5 3
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%
Distinct34
Distinct (%)0.6%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:30.819108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.968703704
Min length1

Characters and Unicode

Total characters21431
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.4%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row-53
ValueCountFrequency (%)
9999 5285
97.9%
53 68
 
1.3%
32 3
 
0.1%
34 3
 
0.1%
8 3
 
0.1%
23 3
 
0.1%
22 2
 
< 0.1%
16 2
 
< 0.1%
11 2
 
< 0.1%
17 2
 
< 0.1%
Other values (24) 27
 
0.5%
2024-01-27T14:09:31.052404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21142
98.7%
3 84
 
0.4%
5 75
 
0.3%
- 68
 
0.3%
2 16
 
0.1%
4 11
 
0.1%
1 11
 
0.1%
6 10
 
< 0.1%
7 6
 
< 0.1%
8 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21363
99.7%
Dash Punctuation 68
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21142
99.0%
3 84
 
0.4%
5 75
 
0.4%
2 16
 
0.1%
4 11
 
0.1%
1 11
 
0.1%
6 10
 
< 0.1%
7 6
 
< 0.1%
8 5
 
< 0.1%
0 3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21142
98.7%
3 84
 
0.4%
5 75
 
0.3%
- 68
 
0.3%
2 16
 
0.1%
4 11
 
0.1%
1 11
 
0.1%
6 10
 
< 0.1%
7 6
 
< 0.1%
8 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21142
98.7%
3 84
 
0.4%
5 75
 
0.3%
- 68
 
0.3%
2 16
 
0.1%
4 11
 
0.1%
1 11
 
0.1%
6 10
 
< 0.1%
7 6
 
< 0.1%
8 5
 
< 0.1%
Distinct20
Distinct (%)0.4%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:31.157713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.987777778
Min length1

Characters and Unicode

Total characters21534
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.3%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5359
99.2%
53 19
 
0.4%
26 3
 
0.1%
17 2
 
< 0.1%
8 2
 
< 0.1%
34 2
 
< 0.1%
19 1
 
< 0.1%
41 1
 
< 0.1%
21 1
 
< 0.1%
32 1
 
< 0.1%
Other values (9) 9
 
0.2%
2024-01-27T14:09:31.423851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21437
99.5%
3 26
 
0.1%
5 21
 
0.1%
- 18
 
0.1%
1 8
 
< 0.1%
2 7
 
< 0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21516
99.9%
Dash Punctuation 18
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21437
99.6%
3 26
 
0.1%
5 21
 
0.1%
1 8
 
< 0.1%
2 7
 
< 0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
0 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21437
99.5%
3 26
 
0.1%
5 21
 
0.1%
- 18
 
0.1%
1 8
 
< 0.1%
2 7
 
< 0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21437
99.5%
3 26
 
0.1%
5 21
 
0.1%
- 18
 
0.1%
1 8
 
< 0.1%
2 7
 
< 0.1%
4 5
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
Distinct147
Distinct (%)2.7%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:31.641958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.574444444
Min length1

Characters and Unicode

Total characters19302
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st row9999
2nd row53
3rd row9999
4th row45
5th row9999
ValueCountFrequency (%)
9999 4177
77.4%
94 19
 
0.4%
78 19
 
0.4%
21 18
 
0.3%
73 18
 
0.3%
69 18
 
0.3%
77 18
 
0.3%
48 17
 
0.3%
76 17
 
0.3%
85 17
 
0.3%
Other values (137) 1062
 
19.7%
2024-01-27T14:09:31.988806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 16912
87.6%
1 529
 
2.7%
7 277
 
1.4%
4 251
 
1.3%
2 249
 
1.3%
6 240
 
1.2%
8 228
 
1.2%
3 226
 
1.2%
5 209
 
1.1%
0 181
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19302
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 16912
87.6%
1 529
 
2.7%
7 277
 
1.4%
4 251
 
1.3%
2 249
 
1.3%
6 240
 
1.2%
8 228
 
1.2%
3 226
 
1.2%
5 209
 
1.1%
0 181
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 19302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 16912
87.6%
1 529
 
2.7%
7 277
 
1.4%
4 251
 
1.3%
2 249
 
1.3%
6 240
 
1.2%
8 228
 
1.2%
3 226
 
1.2%
5 209
 
1.1%
0 181
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 16912
87.6%
1 529
 
2.7%
7 277
 
1.4%
4 251
 
1.3%
2 249
 
1.3%
6 240
 
1.2%
8 228
 
1.2%
3 226
 
1.2%
5 209
 
1.1%
0 181
 
0.9%
Distinct104
Distinct (%)1.9%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:32.166425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.770925926
Min length1

Characters and Unicode

Total characters20363
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.4%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 4775
88.4%
78 67
 
1.2%
38 53
 
1.0%
20 40
 
0.7%
53 22
 
0.4%
32 19
 
0.4%
8 18
 
0.3%
81 18
 
0.3%
68 17
 
0.3%
44 16
 
0.3%
Other values (93) 355
 
6.6%
2024-01-27T14:09:32.432769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 19160
94.1%
8 208
 
1.0%
2 163
 
0.8%
3 159
 
0.8%
1 138
 
0.7%
7 120
 
0.6%
4 108
 
0.5%
6 106
 
0.5%
5 96
 
0.5%
0 87
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20345
99.9%
Dash Punctuation 18
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 19160
94.2%
8 208
 
1.0%
2 163
 
0.8%
3 159
 
0.8%
1 138
 
0.7%
7 120
 
0.6%
4 108
 
0.5%
6 106
 
0.5%
5 96
 
0.5%
0 87
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 19160
94.1%
8 208
 
1.0%
2 163
 
0.8%
3 159
 
0.8%
1 138
 
0.7%
7 120
 
0.6%
4 108
 
0.5%
6 106
 
0.5%
5 96
 
0.5%
0 87
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 19160
94.1%
8 208
 
1.0%
2 163
 
0.8%
3 159
 
0.8%
1 138
 
0.7%
7 120
 
0.6%
4 108
 
0.5%
6 106
 
0.5%
5 96
 
0.5%
0 87
 
0.4%
Distinct61
Distinct (%)1.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:32.561568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.933148148
Min length1

Characters and Unicode

Total characters21239
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5220
96.7%
68 16
 
0.3%
26 12
 
0.2%
44 11
 
0.2%
20 11
 
0.2%
38 10
 
0.2%
8 10
 
0.2%
96 7
 
0.1%
28 6
 
0.1%
32 6
 
0.1%
Other values (51) 91
 
1.7%
2024-01-27T14:09:32.803864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 20900
98.4%
8 53
 
0.2%
2 50
 
0.2%
6 45
 
0.2%
1 43
 
0.2%
4 39
 
0.2%
3 38
 
0.2%
0 33
 
0.2%
5 27
 
0.1%
7 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 20900
98.4%
8 53
 
0.2%
2 50
 
0.2%
6 45
 
0.2%
1 43
 
0.2%
4 39
 
0.2%
3 38
 
0.2%
0 33
 
0.2%
5 27
 
0.1%
7 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 20900
98.4%
8 53
 
0.2%
2 50
 
0.2%
6 45
 
0.2%
1 43
 
0.2%
4 39
 
0.2%
3 38
 
0.2%
0 33
 
0.2%
5 27
 
0.1%
7 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 20900
98.4%
8 53
 
0.2%
2 50
 
0.2%
6 45
 
0.2%
1 43
 
0.2%
4 39
 
0.2%
3 38
 
0.2%
0 33
 
0.2%
5 27
 
0.1%
7 11
 
0.1%
Distinct108
Distinct (%)2.0%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:32.933439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.710740741
Min length1

Characters and Unicode

Total characters20038
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.4%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 4620
85.6%
44 80
 
1.5%
38 60
 
1.1%
20 54
 
1.0%
26 52
 
1.0%
68 45
 
0.8%
62 33
 
0.6%
56 30
 
0.6%
8 28
 
0.5%
93 22
 
0.4%
Other values (98) 376
 
7.0%
2024-01-27T14:09:33.197997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 18583
92.7%
4 237
 
1.2%
2 233
 
1.2%
6 221
 
1.1%
8 189
 
0.9%
3 159
 
0.8%
1 126
 
0.6%
0 123
 
0.6%
5 106
 
0.5%
7 61
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20038
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 18583
92.7%
4 237
 
1.2%
2 233
 
1.2%
6 221
 
1.1%
8 189
 
0.9%
3 159
 
0.8%
1 126
 
0.6%
0 123
 
0.6%
5 106
 
0.5%
7 61
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 20038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 18583
92.7%
4 237
 
1.2%
2 233
 
1.2%
6 221
 
1.1%
8 189
 
0.9%
3 159
 
0.8%
1 126
 
0.6%
0 123
 
0.6%
5 106
 
0.5%
7 61
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 18583
92.7%
4 237
 
1.2%
2 233
 
1.2%
6 221
 
1.1%
8 189
 
0.9%
3 159
 
0.8%
1 126
 
0.6%
0 123
 
0.6%
5 106
 
0.5%
7 61
 
0.3%
Distinct106
Distinct (%)2.0%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:33.342921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.797222222
Min length1

Characters and Unicode

Total characters20505
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.6%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 4852
89.9%
38 58
 
1.1%
20 45
 
0.8%
26 43
 
0.8%
44 37
 
0.7%
8 21
 
0.4%
68 19
 
0.4%
56 17
 
0.3%
32 16
 
0.3%
53 14
 
0.3%
Other values (96) 278
 
5.1%
2024-01-27T14:09:33.608543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 19457
94.9%
2 173
 
0.8%
8 142
 
0.7%
6 138
 
0.7%
3 131
 
0.6%
4 127
 
0.6%
0 109
 
0.5%
5 92
 
0.4%
1 90
 
0.4%
7 46
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20505
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 19457
94.9%
2 173
 
0.8%
8 142
 
0.7%
6 138
 
0.7%
3 131
 
0.6%
4 127
 
0.6%
0 109
 
0.5%
5 92
 
0.4%
1 90
 
0.4%
7 46
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 19457
94.9%
2 173
 
0.8%
8 142
 
0.7%
6 138
 
0.7%
3 131
 
0.6%
4 127
 
0.6%
0 109
 
0.5%
5 92
 
0.4%
1 90
 
0.4%
7 46
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 19457
94.9%
2 173
 
0.8%
8 142
 
0.7%
6 138
 
0.7%
3 131
 
0.6%
4 127
 
0.6%
0 109
 
0.5%
5 92
 
0.4%
1 90
 
0.4%
7 46
 
0.2%
Distinct26
Distinct (%)0.5%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:33.704933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.980740741
Min length1

Characters and Unicode

Total characters21496
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.3%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5341
98.9%
53 22
 
0.4%
8 5
 
0.1%
83 5
 
0.1%
34 2
 
< 0.1%
23 2
 
< 0.1%
19 2
 
< 0.1%
75 2
 
< 0.1%
26 2
 
< 0.1%
17 2
 
< 0.1%
Other values (15) 15
 
0.3%
2024-01-27T14:09:33.913953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21367
99.4%
3 33
 
0.2%
5 28
 
0.1%
- 21
 
0.1%
8 11
 
0.1%
7 9
 
< 0.1%
1 8
 
< 0.1%
2 8
 
< 0.1%
4 6
 
< 0.1%
6 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21475
99.9%
Dash Punctuation 21
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21367
99.5%
3 33
 
0.2%
5 28
 
0.1%
8 11
 
0.1%
7 9
 
< 0.1%
1 8
 
< 0.1%
2 8
 
< 0.1%
4 6
 
< 0.1%
6 4
 
< 0.1%
0 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21496
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21367
99.4%
3 33
 
0.2%
5 28
 
0.1%
- 21
 
0.1%
8 11
 
0.1%
7 9
 
< 0.1%
1 8
 
< 0.1%
2 8
 
< 0.1%
4 6
 
< 0.1%
6 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21367
99.4%
3 33
 
0.2%
5 28
 
0.1%
- 21
 
0.1%
8 11
 
0.1%
7 9
 
< 0.1%
1 8
 
< 0.1%
2 8
 
< 0.1%
4 6
 
< 0.1%
6 4
 
< 0.1%
Distinct62
Distinct (%)1.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:34.043353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.880185185
Min length1

Characters and Unicode

Total characters20953
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5114
94.7%
11 17
 
0.3%
9 16
 
0.3%
8 13
 
0.2%
48 12
 
0.2%
12 12
 
0.2%
10 11
 
0.2%
4 10
 
0.2%
2 9
 
0.2%
3 8
 
0.1%
Other values (52) 178
 
3.3%
2024-01-27T14:09:34.289752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 20487
97.8%
1 110
 
0.5%
2 87
 
0.4%
4 72
 
0.3%
3 58
 
0.3%
8 39
 
0.2%
5 29
 
0.1%
7 25
 
0.1%
0 23
 
0.1%
6 23
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20953
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 20487
97.8%
1 110
 
0.5%
2 87
 
0.4%
4 72
 
0.3%
3 58
 
0.3%
8 39
 
0.2%
5 29
 
0.1%
7 25
 
0.1%
0 23
 
0.1%
6 23
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 20487
97.8%
1 110
 
0.5%
2 87
 
0.4%
4 72
 
0.3%
3 58
 
0.3%
8 39
 
0.2%
5 29
 
0.1%
7 25
 
0.1%
0 23
 
0.1%
6 23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 20487
97.8%
1 110
 
0.5%
2 87
 
0.4%
4 72
 
0.3%
3 58
 
0.3%
8 39
 
0.2%
5 29
 
0.1%
7 25
 
0.1%
0 23
 
0.1%
6 23
 
0.1%
Distinct74
Distinct (%)1.4%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:34.415192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.960185185
Min length1

Characters and Unicode

Total characters21385
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)0.9%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5290
98.0%
56 4
 
0.1%
23 4
 
0.1%
15 4
 
0.1%
83 3
 
0.1%
24 3
 
0.1%
22 3
 
0.1%
42 3
 
0.1%
47 3
 
0.1%
70 2
 
< 0.1%
Other values (64) 81
 
1.5%
2024-01-27T14:09:34.674263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21176
99.0%
1 36
 
0.2%
2 33
 
0.2%
5 30
 
0.1%
4 27
 
0.1%
3 26
 
0.1%
6 20
 
0.1%
7 14
 
0.1%
8 13
 
0.1%
0 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21385
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21176
99.0%
1 36
 
0.2%
2 33
 
0.2%
5 30
 
0.1%
4 27
 
0.1%
3 26
 
0.1%
6 20
 
0.1%
7 14
 
0.1%
8 13
 
0.1%
0 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21385
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21176
99.0%
1 36
 
0.2%
2 33
 
0.2%
5 30
 
0.1%
4 27
 
0.1%
3 26
 
0.1%
6 20
 
0.1%
7 14
 
0.1%
8 13
 
0.1%
0 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21176
99.0%
1 36
 
0.2%
2 33
 
0.2%
5 30
 
0.1%
4 27
 
0.1%
3 26
 
0.1%
6 20
 
0.1%
7 14
 
0.1%
8 13
 
0.1%
0 10
 
< 0.1%

n_months_since_lapse_32c74c
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing12592
Missing (%)70.0%
Memory size281.1 KiB
2024-01-27T14:09:34.762873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21600
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 5400
100.0%
2024-01-27T14:09:34.940508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 21600
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 21600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 21600
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 21600
100.0%

f_ever_bought_839f8a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1191640729
Minimum0
Maximum1
Zeros15848
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:35.045248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3239904796
Coefficient of variation (CV)2.718860406
Kurtosis3.528390185
Mean0.1191640729
Median Absolute Deviation (MAD)0
Skewness2.351169492
Sum2144
Variance0.1049698309
MonotonicityNot monotonic
2024-01-27T14:09:35.141536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 15848
88.1%
1 2144
 
11.9%
ValueCountFrequency (%)
0 15848
88.1%
1 2144
 
11.9%
ValueCountFrequency (%)
1 2144
 
11.9%
0 15848
88.1%

f_ever_bought_e22a6a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2565028902
Minimum0
Maximum1
Zeros13377
Zeros (%)74.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:35.237231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4367147327
Coefficient of variation (CV)1.702572366
Kurtosis-0.7562900044
Mean0.2565028902
Median Absolute Deviation (MAD)0
Skewness1.115255163
Sum4615
Variance0.1907197578
MonotonicityNot monotonic
2024-01-27T14:09:35.341938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 13377
74.3%
1 4615
 
25.7%
ValueCountFrequency (%)
0 13377
74.3%
1 4615
 
25.7%
ValueCountFrequency (%)
1 4615
 
25.7%
0 13377
74.3%

f_ever_bought_d0adeb
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:35.437947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:35.526481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%

f_ever_bought_c4bda5
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004557581147
Minimum0
Maximum1
Zeros17910
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:35.616182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06735771502
Coefficient of variation (CV)14.7792684
Kurtosis214.4791455
Mean0.004557581147
Median Absolute Deviation (MAD)0
Skewness14.71242005
Sum82
Variance0.004537061772
MonotonicityNot monotonic
2024-01-27T14:09:35.712154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17910
99.5%
1 82
 
0.5%
ValueCountFrequency (%)
0 17910
99.5%
1 82
 
0.5%
ValueCountFrequency (%)
1 82
 
0.5%
0 17910
99.5%

f_ever_bought_ltc
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1895842597
Minimum0
Maximum1
Zeros14581
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:35.808379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3919829181
Coefficient of variation (CV)2.067592103
Kurtosis0.5091090081
Mean0.1895842597
Median Absolute Deviation (MAD)0
Skewness1.583998872
Sum3411
Variance0.1536506081
MonotonicityNot monotonic
2024-01-27T14:09:35.913762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14581
81.0%
1 3411
 
19.0%
ValueCountFrequency (%)
0 14581
81.0%
1 3411
 
19.0%
ValueCountFrequency (%)
1 3411
 
19.0%
0 14581
81.0%

f_ever_bought_507c37
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3891173855
Minimum0
Maximum1
Zeros10991
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:36.010848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4875635941
Coefficient of variation (CV)1.252998741
Kurtosis-1.79327056
Mean0.3891173855
Median Absolute Deviation (MAD)0
Skewness0.454894266
Sum7001
Variance0.2377182582
MonotonicityNot monotonic
2024-01-27T14:09:36.108531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 10991
61.1%
1 7001
38.9%
ValueCountFrequency (%)
0 10991
61.1%
1 7001
38.9%
ValueCountFrequency (%)
1 7001
38.9%
0 10991
61.1%

f_ever_bought_gi
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1172743442
Minimum0
Maximum1
Zeros15882
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:36.202040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3217558491
Coefficient of variation (CV)2.7436167
Kurtosis3.661219864
Mean0.1172743442
Median Absolute Deviation (MAD)0
Skewness2.379246287
Sum2110
Variance0.1035268264
MonotonicityNot monotonic
2024-01-27T14:09:36.294289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 15882
88.3%
1 2110
 
11.7%
ValueCountFrequency (%)
0 15882
88.3%
1 2110
 
11.7%
ValueCountFrequency (%)
1 2110
 
11.7%
0 15882
88.3%
Distinct322
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:36.463271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.753168075
Min length1

Characters and Unicode

Total characters67527
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 15743
87.5%
8 254
 
1.4%
84 110
 
0.6%
5 94
 
0.5%
50 72
 
0.4%
36 46
 
0.3%
10 40
 
0.2%
32 34
 
0.2%
6 33
 
0.2%
38 30
 
0.2%
Other values (312) 1536
 
8.5%
2024-01-27T14:09:36.777920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 63217
93.6%
1 698
 
1.0%
8 625
 
0.9%
2 544
 
0.8%
3 521
 
0.8%
5 491
 
0.7%
4 468
 
0.7%
6 358
 
0.5%
0 311
 
0.5%
7 294
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67527
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 63217
93.6%
1 698
 
1.0%
8 625
 
0.9%
2 544
 
0.8%
3 521
 
0.8%
5 491
 
0.7%
4 468
 
0.7%
6 358
 
0.5%
0 311
 
0.5%
7 294
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 67527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 63217
93.6%
1 698
 
1.0%
8 625
 
0.9%
2 544
 
0.8%
3 521
 
0.8%
5 491
 
0.7%
4 468
 
0.7%
6 358
 
0.5%
0 311
 
0.5%
7 294
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 63217
93.6%
1 698
 
1.0%
8 625
 
0.9%
2 544
 
0.8%
3 521
 
0.8%
5 491
 
0.7%
4 468
 
0.7%
6 358
 
0.5%
0 311
 
0.5%
7 294
 
0.4%
Distinct219
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:36.996242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.383003557
Min length1

Characters and Unicode

Total characters60867
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row140
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 12328
68.5%
8 1179
 
6.6%
5 184
 
1.0%
11 95
 
0.5%
1 85
 
0.5%
20 78
 
0.4%
2 71
 
0.4%
6 69
 
0.4%
14 68
 
0.4%
4 64
 
0.4%
Other values (209) 3771
 
21.0%
2024-01-27T14:09:37.342528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 50034
82.2%
1 2953
 
4.9%
8 1906
 
3.1%
2 1140
 
1.9%
5 955
 
1.6%
0 857
 
1.4%
3 821
 
1.3%
4 807
 
1.3%
6 761
 
1.3%
7 633
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60867
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 50034
82.2%
1 2953
 
4.9%
8 1906
 
3.1%
2 1140
 
1.9%
5 955
 
1.6%
0 857
 
1.4%
3 821
 
1.3%
4 807
 
1.3%
6 761
 
1.3%
7 633
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 50034
82.2%
1 2953
 
4.9%
8 1906
 
3.1%
2 1140
 
1.9%
5 955
 
1.6%
0 857
 
1.4%
3 821
 
1.3%
4 807
 
1.3%
6 761
 
1.3%
7 633
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 50034
82.2%
1 2953
 
4.9%
8 1906
 
3.1%
2 1140
 
1.9%
5 955
 
1.6%
0 857
 
1.4%
3 821
 
1.3%
4 807
 
1.3%
6 761
 
1.3%
7 633
 
1.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:37.439442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters71968
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17992
100.0%
2024-01-27T14:09:37.650173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71968
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71968
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71968
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71968
100.0%
Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:37.763222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.986716318
Min length1

Characters and Unicode

Total characters71729
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17876
99.4%
5 13
 
0.1%
3 8
 
< 0.1%
2 7
 
< 0.1%
10 7
 
< 0.1%
14 6
 
< 0.1%
1 5
 
< 0.1%
12 5
 
< 0.1%
117 5
 
< 0.1%
115 4
 
< 0.1%
Other values (32) 56
 
0.3%
2024-01-27T14:09:37.996805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71729
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71729
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
Distinct74
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:38.125546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.582814584
Min length1

Characters and Unicode

Total characters64462
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.2%

Sample

1st row6
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 14556
80.9%
31 288
 
1.6%
33 284
 
1.6%
32 194
 
1.1%
30 159
 
0.9%
17 130
 
0.7%
14 121
 
0.7%
15 119
 
0.7%
28 113
 
0.6%
16 107
 
0.6%
Other values (64) 1921
 
10.7%
2024-01-27T14:09:38.553655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 58483
90.7%
1 1498
 
2.3%
3 1487
 
2.3%
2 1187
 
1.8%
4 326
 
0.5%
7 314
 
0.5%
8 314
 
0.5%
0 303
 
0.5%
5 280
 
0.4%
6 270
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 58483
90.7%
1 1498
 
2.3%
3 1487
 
2.3%
2 1187
 
1.8%
4 326
 
0.5%
7 314
 
0.5%
8 314
 
0.5%
0 303
 
0.5%
5 280
 
0.4%
6 270
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 64462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 58483
90.7%
1 1498
 
2.3%
3 1487
 
2.3%
2 1187
 
1.8%
4 326
 
0.5%
7 314
 
0.5%
8 314
 
0.5%
0 303
 
0.5%
5 280
 
0.4%
6 270
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 58483
90.7%
1 1498
 
2.3%
3 1487
 
2.3%
2 1187
 
1.8%
4 326
 
0.5%
7 314
 
0.5%
8 314
 
0.5%
0 303
 
0.5%
5 280
 
0.4%
6 270
 
0.4%
Distinct464
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:38.765187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.966874166
Min length1

Characters and Unicode

Total characters53380
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row62
ValueCountFrequency (%)
9999 8938
49.7%
8 1755
 
9.8%
13 449
 
2.5%
5 297
 
1.7%
82 209
 
1.2%
50 200
 
1.1%
84 178
 
1.0%
2 167
 
0.9%
1 147
 
0.8%
11 144
 
0.8%
Other values (454) 5508
30.6%
2024-01-27T14:09:39.128368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 36694
68.7%
1 3379
 
6.3%
8 3079
 
5.8%
2 2136
 
4.0%
3 1928
 
3.6%
4 1558
 
2.9%
5 1480
 
2.8%
0 1154
 
2.2%
6 1088
 
2.0%
7 884
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53380
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 36694
68.7%
1 3379
 
6.3%
8 3079
 
5.8%
2 2136
 
4.0%
3 1928
 
3.6%
4 1558
 
2.9%
5 1480
 
2.8%
0 1154
 
2.2%
6 1088
 
2.0%
7 884
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 53380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 36694
68.7%
1 3379
 
6.3%
8 3079
 
5.8%
2 2136
 
4.0%
3 1928
 
3.6%
4 1558
 
2.9%
5 1480
 
2.8%
0 1154
 
2.2%
6 1088
 
2.0%
7 884
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 36694
68.7%
1 3379
 
6.3%
8 3079
 
5.8%
2 2136
 
4.0%
3 1928
 
3.6%
4 1558
 
2.9%
5 1480
 
2.8%
0 1154
 
2.2%
6 1088
 
2.0%
7 884
 
1.7%
Distinct147
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:39.304951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.660404624
Min length1

Characters and Unicode

Total characters65858
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 15372
85.4%
2 188
 
1.0%
3 140
 
0.8%
1 135
 
0.8%
4 111
 
0.6%
9 100
 
0.6%
5 94
 
0.5%
8 86
 
0.5%
6 80
 
0.4%
10 72
 
0.4%
Other values (137) 1614
 
9.0%
2024-01-27T14:09:39.587648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 61854
93.9%
1 804
 
1.2%
4 518
 
0.8%
5 478
 
0.7%
2 474
 
0.7%
3 429
 
0.7%
6 409
 
0.6%
7 329
 
0.5%
8 326
 
0.5%
0 237
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 61854
93.9%
1 804
 
1.2%
4 518
 
0.8%
5 478
 
0.7%
2 474
 
0.7%
3 429
 
0.7%
6 409
 
0.6%
7 329
 
0.5%
8 326
 
0.5%
0 237
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 65858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 61854
93.9%
1 804
 
1.2%
4 518
 
0.8%
5 478
 
0.7%
2 474
 
0.7%
3 429
 
0.7%
6 409
 
0.6%
7 329
 
0.5%
8 326
 
0.5%
0 237
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 61854
93.9%
1 804
 
1.2%
4 518
 
0.8%
5 478
 
0.7%
2 474
 
0.7%
3 429
 
0.7%
6 409
 
0.6%
7 329
 
0.5%
8 326
 
0.5%
0 237
 
0.4%

f_ever_bought_ltc_1280bf
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:39.710347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:39.798105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%

f_ever_bought_grp_6fc3e6
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02178746109
Minimum0
Maximum1
Zeros17600
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:39.886077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1459929871
Coefficient of variation (CV)6.700780161
Kurtosis40.93193953
Mean0.02178746109
Median Absolute Deviation (MAD)0
Skewness6.551899688
Sum392
Variance0.02131395227
MonotonicityNot monotonic
2024-01-27T14:09:39.984167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17600
97.8%
1 392
 
2.2%
ValueCountFrequency (%)
0 17600
97.8%
1 392
 
2.2%
ValueCountFrequency (%)
1 392
 
2.2%
0 17600
97.8%

f_ever_bought_grp_de05ae
Real number (ℝ)

SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0003890618052
Minimum0
Maximum1
Zeros17985
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:40.087780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01972136033
Coefficient of variation (CV)50.68953071
Kurtosis2565.999476
Mean0.0003890618052
Median Absolute Deviation (MAD)0
Skewness50.67261823
Sum7
Variance0.0003889320531
MonotonicityNot monotonic
2024-01-27T14:09:40.184190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17985
> 99.9%
1 7
 
< 0.1%
ValueCountFrequency (%)
0 17985
> 99.9%
1 7
 
< 0.1%
ValueCountFrequency (%)
1 7
 
< 0.1%
0 17985
> 99.9%

f_ever_bought_inv_dcd836
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:40.282236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:40.371130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%

f_ever_bought_grp_945b5a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07792352157
Minimum0
Maximum1
Zeros16590
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:40.466716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2680586505
Coefficient of variation (CV)3.440022282
Kurtosis7.920138626
Mean0.07792352157
Median Absolute Deviation (MAD)0
Skewness3.149485392
Sum1402
Variance0.0718554401
MonotonicityNot monotonic
2024-01-27T14:09:40.572234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16590
92.2%
1 1402
 
7.8%
ValueCountFrequency (%)
0 16590
92.2%
1 1402
 
7.8%
ValueCountFrequency (%)
1 1402
 
7.8%
0 16590
92.2%

f_ever_bought_grp_6a5788
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007225433526
Minimum0
Maximum1
Zeros17862
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:40.659945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08469725703
Coefficient of variation (CV)11.72210037
Kurtosis133.4446931
Mean0.007225433526
Median Absolute Deviation (MAD)0
Skewness11.63743353
Sum130
Variance0.007173625348
MonotonicityNot monotonic
2024-01-27T14:09:40.755989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17862
99.3%
1 130
 
0.7%
ValueCountFrequency (%)
0 17862
99.3%
1 130
 
0.7%
ValueCountFrequency (%)
1 130
 
0.7%
0 17862
99.3%

f_ever_bought_ltc_43b9d5
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1835815918
Minimum0
Maximum1
Zeros14689
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:40.852704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3871533569
Coefficient of variation (CV)2.108889857
Kurtosis0.6725517333
Mean0.1835815918
Median Absolute Deviation (MAD)0
Skewness1.634771233
Sum3303
Variance0.1498877218
MonotonicityNot monotonic
2024-01-27T14:09:40.957364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14689
81.6%
1 3303
 
18.4%
ValueCountFrequency (%)
0 14689
81.6%
1 3303
 
18.4%
ValueCountFrequency (%)
1 3303
 
18.4%
0 14689
81.6%

f_ever_bought_grp_9cdedf
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02945753668
Minimum0
Maximum1
Zeros17462
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:41.037779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1690898558
Coefficient of variation (CV)5.740122048
Kurtosis28.98590949
Mean0.02945753668
Median Absolute Deviation (MAD)0
Skewness5.56620943
Sum530
Variance0.02859137933
MonotonicityNot monotonic
2024-01-27T14:09:41.141930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17462
97.1%
1 530
 
2.9%
ValueCountFrequency (%)
0 17462
97.1%
1 530
 
2.9%
ValueCountFrequency (%)
1 530
 
2.9%
0 17462
97.1%

f_ever_bought_lh_d0adeb
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:41.238197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:41.326682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%

f_ever_bought_grp_1581d7
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1754112939
Minimum0
Maximum1
Zeros14836
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:41.416229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3803290833
Coefficient of variation (CV)2.168213202
Kurtosis0.9142005018
Mean0.1754112939
Median Absolute Deviation (MAD)0
Skewness1.707073194
Sum3156
Variance0.1446502116
MonotonicityNot monotonic
2024-01-27T14:09:41.504348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14836
82.5%
1 3156
 
17.5%
ValueCountFrequency (%)
0 14836
82.5%
1 3156
 
17.5%
ValueCountFrequency (%)
1 3156
 
17.5%
0 14836
82.5%

f_ever_bought_grp_22decf
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00500222321
Minimum0
Maximum1
Zeros17902
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:41.600655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07055124111
Coefficient of variation (CV)14.103977
Kurtosis194.9706503
Mean0.00500222321
Median Absolute Deviation (MAD)0
Skewness14.03385112
Sum90
Variance0.004977477623
MonotonicityNot monotonic
2024-01-27T14:09:41.697577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17902
99.5%
1 90
 
0.5%
ValueCountFrequency (%)
0 17902
99.5%
1 90
 
0.5%
ValueCountFrequency (%)
1 90
 
0.5%
0 17902
99.5%

f_ever_bought_lh_507c37
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07970208982
Minimum0
Maximum1
Zeros16558
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:41.794070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2708389627
Coefficient of variation (CV)3.398141295
Kurtosis7.635782409
Mean0.07970208982
Median Absolute Deviation (MAD)0
Skewness3.104018947
Sum1434
Variance0.07335374372
MonotonicityNot monotonic
2024-01-27T14:09:41.898225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16558
92.0%
1 1434
 
8.0%
ValueCountFrequency (%)
0 16558
92.0%
1 1434
 
8.0%
ValueCountFrequency (%)
1 1434
 
8.0%
0 16558
92.0%

f_ever_bought_lh_839f8a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03701645176
Minimum0
Maximum1
Zeros17326
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:41.994615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1888073499
Coefficient of variation (CV)5.100633394
Kurtosis22.05991782
Mean0.03701645176
Median Absolute Deviation (MAD)0
Skewness4.904841041
Sum666
Variance0.03564821539
MonotonicityNot monotonic
2024-01-27T14:09:42.091056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17326
96.3%
1 666
 
3.7%
ValueCountFrequency (%)
0 17326
96.3%
1 666
 
3.7%
ValueCountFrequency (%)
1 666
 
3.7%
0 17326
96.3%

f_ever_bought_inv_e9f316
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004557581147
Minimum0
Maximum1
Zeros17910
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:42.187831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06735771502
Coefficient of variation (CV)14.7792684
Kurtosis214.4791455
Mean0.004557581147
Median Absolute Deviation (MAD)0
Skewness14.71242005
Sum82
Variance0.004537061772
MonotonicityNot monotonic
2024-01-27T14:09:42.287253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17910
99.5%
1 82
 
0.5%
ValueCountFrequency (%)
0 17910
99.5%
1 82
 
0.5%
ValueCountFrequency (%)
1 82
 
0.5%
0 17910
99.5%

f_ever_bought_grp_caa6ff
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0401289462
Minimum0
Maximum1
Zeros17270
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:42.380606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1962670499
Coefficient of variation (CV)4.890909642
Kurtosis19.96735618
Mean0.0401289462
Median Absolute Deviation (MAD)0
Skewness4.686697836
Sum722
Variance0.03852075487
MonotonicityNot monotonic
2024-01-27T14:09:42.484639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17270
96.0%
1 722
 
4.0%
ValueCountFrequency (%)
0 17270
96.0%
1 722
 
4.0%
ValueCountFrequency (%)
1 722
 
4.0%
0 17270
96.0%

f_ever_bought_grp_fd3bfb
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0120053357
Minimum0
Maximum1
Zeros17776
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:42.581217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1089121981
Coefficient of variation (CV)9.071982727
Kurtosis78.33054744
Mean0.0120053357
Median Absolute Deviation (MAD)0
Skewness8.962245265
Sum216
Variance0.0118618669
MonotonicityNot monotonic
2024-01-27T14:09:42.678279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17776
98.8%
1 216
 
1.2%
ValueCountFrequency (%)
0 17776
98.8%
1 216
 
1.2%
ValueCountFrequency (%)
1 216
 
1.2%
0 17776
98.8%

f_ever_bought_lh_e22a6a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1934748777
Minimum0
Maximum1
Zeros14511
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:42.775032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3950329388
Coefficient of variation (CV)2.041778982
Kurtosis0.4089637921
Mean0.1934748777
Median Absolute Deviation (MAD)0
Skewness1.552069053
Sum3481
Variance0.1560510228
MonotonicityNot monotonic
2024-01-27T14:09:42.879721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 14511
80.7%
1 3481
 
19.3%
ValueCountFrequency (%)
0 14511
80.7%
1 3481
 
19.3%
ValueCountFrequency (%)
1 3481
 
19.3%
0 14511
80.7%

f_ever_bought_grp_70e1dd
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2228768341
Minimum0
Maximum1
Zeros13982
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:42.970711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4161879121
Coefficient of variation (CV)1.867344866
Kurtosis-0.226149026
Mean0.2228768341
Median Absolute Deviation (MAD)0
Skewness1.33186941
Sum4010
Variance0.1732123781
MonotonicityNot monotonic
2024-01-27T14:09:43.064422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 13982
77.7%
1 4010
 
22.3%
ValueCountFrequency (%)
0 13982
77.7%
1 4010
 
22.3%
ValueCountFrequency (%)
1 4010
 
22.3%
0 13982
77.7%

f_ever_bought_grp_e04c3a
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01017118719
Minimum0
Maximum1
Zeros17809
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:43.153191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1003408877
Coefficient of variation (CV)9.865209024
Kurtosis93.35349009
Mean0.01017118719
Median Absolute Deviation (MAD)0
Skewness9.7643798
Sum183
Variance0.01006829374
MonotonicityNot monotonic
2024-01-27T14:09:43.260662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17809
99.0%
1 183
 
1.0%
ValueCountFrequency (%)
0 17809
99.0%
1 183
 
1.0%
ValueCountFrequency (%)
1 183
 
1.0%
0 17809
99.0%

f_ever_bought_grp_fe5fb8
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07986883059
Minimum0
Maximum1
Zeros16555
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:43.363424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2710975568
Coefficient of variation (CV)3.394284789
Kurtosis7.609778516
Mean0.07986883059
Median Absolute Deviation (MAD)0
Skewness3.099827837
Sum1437
Variance0.0734938853
MonotonicityNot monotonic
2024-01-27T14:09:43.459519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16555
92.0%
1 1437
 
8.0%
ValueCountFrequency (%)
0 16555
92.0%
1 1437
 
8.0%
ValueCountFrequency (%)
1 1437
 
8.0%
0 16555
92.0%

f_ever_bought_grp_94baec
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07258781681
Minimum0
Maximum1
Zeros16686
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:43.547725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2594659274
Coefficient of variation (CV)3.574510693
Kurtosis8.857480524
Mean0.07258781681
Median Absolute Deviation (MAD)0
Skewness3.294919716
Sum1306
Variance0.06732256746
MonotonicityNot monotonic
2024-01-27T14:09:43.645236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16686
92.7%
1 1306
 
7.3%
ValueCountFrequency (%)
0 16686
92.7%
1 1306
 
7.3%
ValueCountFrequency (%)
1 1306
 
7.3%
0 16686
92.7%

f_ever_bought_grp_e91421
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01545131169
Minimum0
Maximum1
Zeros17714
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:43.741423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1233426699
Coefficient of variation (CV)7.98266661
Kurtosis59.75205561
Mean0.01545131169
Median Absolute Deviation (MAD)0
Skewness7.857824988
Sum278
Variance0.01521341423
MonotonicityNot monotonic
2024-01-27T14:09:43.845722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 17714
98.5%
1 278
 
1.5%
ValueCountFrequency (%)
0 17714
98.5%
1 278
 
1.5%
ValueCountFrequency (%)
1 278
 
1.5%
0 17714
98.5%

f_ever_bought_lh_f852af
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08186971988
Minimum0
Maximum1
Zeros16519
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:43.942072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2741737531
Coefficient of variation (CV)3.348903032
Kurtosis7.306061889
Mean0.08186971988
Median Absolute Deviation (MAD)0
Skewness3.050450746
Sum1473
Variance0.07517124688
MonotonicityNot monotonic
2024-01-27T14:09:44.038756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16519
91.8%
1 1473
 
8.2%
ValueCountFrequency (%)
0 16519
91.8%
1 1473
 
8.2%
ValueCountFrequency (%)
1 1473
 
8.2%
0 16519
91.8%

f_ever_bought_lh_947b15
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08692752334
Minimum0
Maximum1
Zeros16428
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:44.126971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2817366514
Coefficient of variation (CV)3.241052322
Kurtosis6.601207433
Mean0.08692752334
Median Absolute Deviation (MAD)0
Skewness2.932656416
Sum1564
Variance0.07937554074
MonotonicityNot monotonic
2024-01-27T14:09:44.231976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 16428
91.3%
1 1564
 
8.7%
ValueCountFrequency (%)
0 16428
91.3%
1 1564
 
8.7%
ValueCountFrequency (%)
1 1564
 
8.7%
0 16428
91.3%

f_ever_bought_32c74c
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros17992
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:44.320571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-01-27T14:09:44.417435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
ValueCountFrequency (%)
0 17992
100.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:44.489668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters71968
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17992
100.0%
2024-01-27T14:09:44.674758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71968
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71968
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71968
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71968
100.0%
Distinct229
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:44.875412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.95914851
Min length1

Characters and Unicode

Total characters71233
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)0.7%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17496
97.2%
64 96
 
0.5%
84 27
 
0.2%
309 16
 
0.1%
95 6
 
< 0.1%
158 4
 
< 0.1%
87 4
 
< 0.1%
173 4
 
< 0.1%
80 4
 
< 0.1%
377 4
 
< 0.1%
Other values (219) 331
 
1.8%
2024-01-27T14:09:45.198417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 70067
98.4%
4 198
 
0.3%
2 171
 
0.2%
6 164
 
0.2%
1 155
 
0.2%
3 137
 
0.2%
8 99
 
0.1%
5 89
 
0.1%
7 84
 
0.1%
0 69
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71233
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 70067
98.4%
4 198
 
0.3%
2 171
 
0.2%
6 164
 
0.2%
1 155
 
0.2%
3 137
 
0.2%
8 99
 
0.1%
5 89
 
0.1%
7 84
 
0.1%
0 69
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 70067
98.4%
4 198
 
0.3%
2 171
 
0.2%
6 164
 
0.2%
1 155
 
0.2%
3 137
 
0.2%
8 99
 
0.1%
5 89
 
0.1%
7 84
 
0.1%
0 69
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 70067
98.4%
4 198
 
0.3%
2 171
 
0.2%
6 164
 
0.2%
1 155
 
0.2%
3 137
 
0.2%
8 99
 
0.1%
5 89
 
0.1%
7 84
 
0.1%
0 69
 
0.1%
Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:45.302486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.998943975
Min length2

Characters and Unicode

Total characters71949
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17980
99.9%
84 3
 
< 0.1%
86 2
 
< 0.1%
124 1
 
< 0.1%
195 1
 
< 0.1%
87 1
 
< 0.1%
309 1
 
< 0.1%
265 1
 
< 0.1%
172 1
 
< 0.1%
80 1
 
< 0.1%
2024-01-27T14:09:45.518719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71922
> 99.9%
8 7
 
< 0.1%
4 4
 
< 0.1%
6 3
 
< 0.1%
1 3
 
< 0.1%
2 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
0 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71922
> 99.9%
8 7
 
< 0.1%
4 4
 
< 0.1%
6 3
 
< 0.1%
1 3
 
< 0.1%
2 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
0 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71922
> 99.9%
8 7
 
< 0.1%
4 4
 
< 0.1%
6 3
 
< 0.1%
1 3
 
< 0.1%
2 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
0 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71922
> 99.9%
8 7
 
< 0.1%
4 4
 
< 0.1%
6 3
 
< 0.1%
1 3
 
< 0.1%
2 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
0 2
 
< 0.1%
3 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:45.606514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters71968
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17992
100.0%
2024-01-27T14:09:45.800727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71968
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71968
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71968
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71968
100.0%
Distinct315
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:45.993572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.842263228
Min length1

Characters and Unicode

Total characters69130
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)0.6%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 16500
91.7%
8 244
 
1.4%
84 103
 
0.6%
5 85
 
0.5%
10 26
 
0.1%
1 22
 
0.1%
32 21
 
0.1%
6 21
 
0.1%
178 20
 
0.1%
11 19
 
0.1%
Other values (305) 931
 
5.2%
2024-01-27T14:09:46.330598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 66184
95.7%
1 553
 
0.8%
8 544
 
0.8%
2 388
 
0.6%
5 306
 
0.4%
3 290
 
0.4%
4 280
 
0.4%
6 208
 
0.3%
7 189
 
0.3%
0 188
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 66184
95.7%
1 553
 
0.8%
8 544
 
0.8%
2 388
 
0.6%
5 306
 
0.4%
3 290
 
0.4%
4 280
 
0.4%
6 208
 
0.3%
7 189
 
0.3%
0 188
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 66184
95.7%
1 553
 
0.8%
8 544
 
0.8%
2 388
 
0.6%
5 306
 
0.4%
3 290
 
0.4%
4 280
 
0.4%
6 208
 
0.3%
7 189
 
0.3%
0 188
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 66184
95.7%
1 553
 
0.8%
8 544
 
0.8%
2 388
 
0.6%
5 306
 
0.4%
3 290
 
0.4%
4 280
 
0.4%
6 208
 
0.3%
7 189
 
0.3%
0 188
 
0.3%
Distinct64
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:46.459240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.980935972
Min length1

Characters and Unicode

Total characters71625
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.2%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17835
99.1%
8 36
 
0.2%
5 16
 
0.1%
68 7
 
< 0.1%
11 7
 
< 0.1%
84 6
 
< 0.1%
6 5
 
< 0.1%
2 5
 
< 0.1%
32 4
 
< 0.1%
14 3
 
< 0.1%
Other values (54) 68
 
0.4%
2024-01-27T14:09:46.708426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71351
99.6%
8 65
 
0.1%
1 56
 
0.1%
5 31
 
< 0.1%
2 29
 
< 0.1%
6 23
 
< 0.1%
3 23
 
< 0.1%
4 20
 
< 0.1%
7 15
 
< 0.1%
0 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71625
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71351
99.6%
8 65
 
0.1%
1 56
 
0.1%
5 31
 
< 0.1%
2 29
 
< 0.1%
6 23
 
< 0.1%
3 23
 
< 0.1%
4 20
 
< 0.1%
7 15
 
< 0.1%
0 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71625
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71351
99.6%
8 65
 
0.1%
1 56
 
0.1%
5 31
 
< 0.1%
2 29
 
< 0.1%
6 23
 
< 0.1%
3 23
 
< 0.1%
4 20
 
< 0.1%
7 15
 
< 0.1%
0 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71351
99.6%
8 65
 
0.1%
1 56
 
0.1%
5 31
 
< 0.1%
2 29
 
< 0.1%
6 23
 
< 0.1%
3 23
 
< 0.1%
4 20
 
< 0.1%
7 15
 
< 0.1%
0 12
 
< 0.1%
Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:46.821620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.599766563
Min length1

Characters and Unicode

Total characters64767
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 14689
81.6%
31 288
 
1.6%
33 284
 
1.6%
32 195
 
1.1%
30 159
 
0.9%
17 128
 
0.7%
15 119
 
0.7%
14 118
 
0.7%
28 113
 
0.6%
16 106
 
0.6%
Other values (26) 1793
 
10.0%
2024-01-27T14:09:47.055168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 59006
91.1%
3 1474
 
2.3%
1 1454
 
2.2%
2 1169
 
1.8%
4 310
 
0.5%
7 299
 
0.5%
0 298
 
0.5%
8 256
 
0.4%
5 252
 
0.4%
6 249
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64767
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 59006
91.1%
3 1474
 
2.3%
1 1454
 
2.2%
2 1169
 
1.8%
4 310
 
0.5%
7 299
 
0.5%
0 298
 
0.5%
8 256
 
0.4%
5 252
 
0.4%
6 249
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 64767
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 59006
91.1%
3 1474
 
2.3%
1 1454
 
2.2%
2 1169
 
1.8%
4 310
 
0.5%
7 299
 
0.5%
0 298
 
0.5%
8 256
 
0.4%
5 252
 
0.4%
6 249
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 59006
91.1%
3 1474
 
2.3%
1 1454
 
2.2%
2 1169
 
1.8%
4 310
 
0.5%
7 299
 
0.5%
0 298
 
0.5%
8 256
 
0.4%
5 252
 
0.4%
6 249
 
0.4%
Distinct245
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:47.279510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.95264562
Min length1

Characters and Unicode

Total characters71116
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)0.8%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17440
96.9%
50 53
 
0.3%
84 42
 
0.2%
8 26
 
0.1%
309 15
 
0.1%
81 8
 
< 0.1%
95 6
 
< 0.1%
173 5
 
< 0.1%
87 5
 
< 0.1%
256 5
 
< 0.1%
Other values (235) 387
 
2.2%
2024-01-27T14:09:47.625139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 69847
98.2%
2 202
 
0.3%
1 188
 
0.3%
5 161
 
0.2%
8 153
 
0.2%
3 149
 
0.2%
0 127
 
0.2%
4 125
 
0.2%
7 91
 
0.1%
6 73
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71116
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 69847
98.2%
2 202
 
0.3%
1 188
 
0.3%
5 161
 
0.2%
8 153
 
0.2%
3 149
 
0.2%
0 127
 
0.2%
4 125
 
0.2%
7 91
 
0.1%
6 73
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 69847
98.2%
2 202
 
0.3%
1 188
 
0.3%
5 161
 
0.2%
8 153
 
0.2%
3 149
 
0.2%
0 127
 
0.2%
4 125
 
0.2%
7 91
 
0.1%
6 73
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 69847
98.2%
2 202
 
0.3%
1 188
 
0.3%
5 161
 
0.2%
8 153
 
0.2%
3 149
 
0.2%
0 127
 
0.2%
4 125
 
0.2%
7 91
 
0.1%
6 73
 
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:47.729442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters71968
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17992
100.0%
2024-01-27T14:09:47.921989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71968
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71968
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71968
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71968
100.0%
Distinct389
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:48.107481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.685471321
Min length1

Characters and Unicode

Total characters66309
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 14609
81.2%
8 180
 
1.0%
84 136
 
0.8%
64 116
 
0.6%
12 76
 
0.4%
178 71
 
0.4%
5 70
 
0.4%
37 62
 
0.3%
50 55
 
0.3%
93 55
 
0.3%
Other values (379) 2562
 
14.2%
2024-01-27T14:09:48.446055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 59045
89.0%
1 1611
 
2.4%
2 913
 
1.4%
8 850
 
1.3%
3 803
 
1.2%
4 757
 
1.1%
6 611
 
0.9%
5 601
 
0.9%
0 580
 
0.9%
7 538
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66309
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 59045
89.0%
1 1611
 
2.4%
2 913
 
1.4%
8 850
 
1.3%
3 803
 
1.2%
4 757
 
1.1%
6 611
 
0.9%
5 601
 
0.9%
0 580
 
0.9%
7 538
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 66309
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 59045
89.0%
1 1611
 
2.4%
2 913
 
1.4%
8 850
 
1.3%
3 803
 
1.2%
4 757
 
1.1%
6 611
 
0.9%
5 601
 
0.9%
0 580
 
0.9%
7 538
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 59045
89.0%
1 1611
 
2.4%
2 913
 
1.4%
8 850
 
1.3%
3 803
 
1.2%
4 757
 
1.1%
6 611
 
0.9%
5 601
 
0.9%
0 580
 
0.9%
7 538
 
0.8%
Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:48.565984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.916963095
Min length1

Characters and Unicode

Total characters70474
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17438
96.9%
8 302
 
1.7%
5 49
 
0.3%
1 23
 
0.1%
20 23
 
0.1%
38 13
 
0.1%
11 12
 
0.1%
50 10
 
0.1%
32 9
 
0.1%
10 9
 
0.1%
Other values (42) 104
 
0.6%
2024-01-27T14:09:48.822980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 69759
99.0%
8 327
 
0.5%
1 103
 
0.1%
5 74
 
0.1%
0 57
 
0.1%
2 55
 
0.1%
3 40
 
0.1%
4 31
 
< 0.1%
6 21
 
< 0.1%
7 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70474
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 69759
99.0%
8 327
 
0.5%
1 103
 
0.1%
5 74
 
0.1%
0 57
 
0.1%
2 55
 
0.1%
3 40
 
0.1%
4 31
 
< 0.1%
6 21
 
< 0.1%
7 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 69759
99.0%
8 327
 
0.5%
1 103
 
0.1%
5 74
 
0.1%
0 57
 
0.1%
2 55
 
0.1%
3 40
 
0.1%
4 31
 
< 0.1%
6 21
 
< 0.1%
7 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 69759
99.0%
8 327
 
0.5%
1 103
 
0.1%
5 74
 
0.1%
0 57
 
0.1%
2 55
 
0.1%
3 40
 
0.1%
4 31
 
< 0.1%
6 21
 
< 0.1%
7 7
 
< 0.1%
Distinct259
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:49.031675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.878557137
Min length1

Characters and Unicode

Total characters69783
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.3%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row62
ValueCountFrequency (%)
9999 16557
92.0%
3 47
 
0.3%
2 38
 
0.2%
1 28
 
0.2%
94 22
 
0.1%
98 20
 
0.1%
100 20
 
0.1%
97 17
 
0.1%
95 17
 
0.1%
122 16
 
0.1%
Other values (249) 1210
 
6.7%
2024-01-27T14:09:49.554370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 66532
95.3%
1 986
 
1.4%
2 486
 
0.7%
3 352
 
0.5%
4 286
 
0.4%
0 267
 
0.4%
8 256
 
0.4%
5 233
 
0.3%
6 197
 
0.3%
7 188
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 66532
95.3%
1 986
 
1.4%
2 486
 
0.7%
3 352
 
0.5%
4 286
 
0.4%
0 267
 
0.4%
8 256
 
0.4%
5 233
 
0.3%
6 197
 
0.3%
7 188
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 66532
95.3%
1 986
 
1.4%
2 486
 
0.7%
3 352
 
0.5%
4 286
 
0.4%
0 267
 
0.4%
8 256
 
0.4%
5 233
 
0.3%
6 197
 
0.3%
7 188
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 66532
95.3%
1 986
 
1.4%
2 486
 
0.7%
3 352
 
0.5%
4 286
 
0.4%
0 267
 
0.4%
8 256
 
0.4%
5 233
 
0.3%
6 197
 
0.3%
7 188
 
0.3%
Distinct99
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:49.716063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.92035349
Min length1

Characters and Unicode

Total characters70535
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17309
96.2%
36 43
 
0.2%
37 19
 
0.1%
35 17
 
0.1%
38 16
 
0.1%
42 16
 
0.1%
48 15
 
0.1%
45 15
 
0.1%
44 15
 
0.1%
10 14
 
0.1%
Other values (89) 513
 
2.9%
2024-01-27T14:09:49.992133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 69293
98.2%
3 218
 
0.3%
4 177
 
0.3%
2 167
 
0.2%
6 155
 
0.2%
1 151
 
0.2%
5 132
 
0.2%
7 105
 
0.1%
8 76
 
0.1%
0 61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70535
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 69293
98.2%
3 218
 
0.3%
4 177
 
0.3%
2 167
 
0.2%
6 155
 
0.2%
1 151
 
0.2%
5 132
 
0.2%
7 105
 
0.1%
8 76
 
0.1%
0 61
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 69293
98.2%
3 218
 
0.3%
4 177
 
0.3%
2 167
 
0.2%
6 155
 
0.2%
1 151
 
0.2%
5 132
 
0.2%
7 105
 
0.1%
8 76
 
0.1%
0 61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 69293
98.2%
3 218
 
0.3%
4 177
 
0.3%
2 167
 
0.2%
6 155
 
0.2%
1 151
 
0.2%
5 132
 
0.2%
7 105
 
0.1%
8 76
 
0.1%
0 61
 
0.1%
Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:50.126774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.986716318
Min length1

Characters and Unicode

Total characters71729
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17876
99.4%
5 13
 
0.1%
3 8
 
< 0.1%
2 7
 
< 0.1%
10 7
 
< 0.1%
14 6
 
< 0.1%
1 5
 
< 0.1%
12 5
 
< 0.1%
117 5
 
< 0.1%
115 4
 
< 0.1%
Other values (32) 56
 
0.3%
2024-01-27T14:09:50.344611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71729
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71729
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71514
99.7%
1 93
 
0.1%
2 28
 
< 0.1%
5 20
 
< 0.1%
4 19
 
< 0.1%
3 18
 
< 0.1%
0 15
 
< 0.1%
6 9
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
Distinct247
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:50.562879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.927634504
Min length1

Characters and Unicode

Total characters70666
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17192
95.6%
84 86
 
0.5%
50 67
 
0.4%
178 12
 
0.1%
80 10
 
0.1%
37 8
 
< 0.1%
11 8
 
< 0.1%
153 8
 
< 0.1%
1 7
 
< 0.1%
7 7
 
< 0.1%
Other values (237) 587
 
3.3%
2024-01-27T14:09:50.893592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 68887
97.5%
1 340
 
0.5%
2 235
 
0.3%
5 218
 
0.3%
8 208
 
0.3%
4 203
 
0.3%
3 178
 
0.3%
0 163
 
0.2%
7 127
 
0.2%
6 107
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70666
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 68887
97.5%
1 340
 
0.5%
2 235
 
0.3%
5 218
 
0.3%
8 208
 
0.3%
4 203
 
0.3%
3 178
 
0.3%
0 163
 
0.2%
7 127
 
0.2%
6 107
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 70666
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 68887
97.5%
1 340
 
0.5%
2 235
 
0.3%
5 218
 
0.3%
8 208
 
0.3%
4 203
 
0.3%
3 178
 
0.3%
0 163
 
0.2%
7 127
 
0.2%
6 107
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 68887
97.5%
1 340
 
0.5%
2 235
 
0.3%
5 218
 
0.3%
8 208
 
0.3%
4 203
 
0.3%
3 178
 
0.3%
0 163
 
0.2%
7 127
 
0.2%
6 107
 
0.2%
Distinct141
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:51.086198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.98065807
Min length1

Characters and Unicode

Total characters71620
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)0.6%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17761
98.7%
50 29
 
0.2%
84 23
 
0.1%
46 3
 
< 0.1%
258 3
 
< 0.1%
243 3
 
< 0.1%
227 3
 
< 0.1%
269 3
 
< 0.1%
80 3
 
< 0.1%
173 3
 
< 0.1%
Other values (131) 158
 
0.9%
2024-01-27T14:09:51.407236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 71076
99.2%
2 89
 
0.1%
1 78
 
0.1%
5 76
 
0.1%
4 60
 
0.1%
0 59
 
0.1%
8 59
 
0.1%
3 56
 
0.1%
6 34
 
< 0.1%
7 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 71076
99.2%
2 89
 
0.1%
1 78
 
0.1%
5 76
 
0.1%
4 60
 
0.1%
0 59
 
0.1%
8 59
 
0.1%
3 56
 
0.1%
6 34
 
< 0.1%
7 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 71076
99.2%
2 89
 
0.1%
1 78
 
0.1%
5 76
 
0.1%
4 60
 
0.1%
0 59
 
0.1%
8 59
 
0.1%
3 56
 
0.1%
6 34
 
< 0.1%
7 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 71076
99.2%
2 89
 
0.1%
1 78
 
0.1%
5 76
 
0.1%
4 60
 
0.1%
0 59
 
0.1%
8 59
 
0.1%
3 56
 
0.1%
6 34
 
< 0.1%
7 33
 
< 0.1%
Distinct219
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:51.640325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.724488662
Min length1

Characters and Unicode

Total characters67011
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row140
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 14511
80.7%
213 39
 
0.2%
100 39
 
0.2%
82 37
 
0.2%
99 36
 
0.2%
96 36
 
0.2%
130 36
 
0.2%
136 33
 
0.2%
90 33
 
0.2%
145 32
 
0.2%
Other values (209) 3160
 
17.6%
2024-01-27T14:09:51.985811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 58742
87.7%
1 2612
 
3.9%
2 968
 
1.4%
0 704
 
1.1%
5 703
 
1.0%
3 693
 
1.0%
4 677
 
1.0%
8 661
 
1.0%
6 651
 
1.0%
7 600
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67011
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 58742
87.7%
1 2612
 
3.9%
2 968
 
1.4%
0 704
 
1.1%
5 703
 
1.0%
3 693
 
1.0%
4 677
 
1.0%
8 661
 
1.0%
6 651
 
1.0%
7 600
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 67011
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 58742
87.7%
1 2612
 
3.9%
2 968
 
1.4%
0 704
 
1.1%
5 703
 
1.0%
3 693
 
1.0%
4 677
 
1.0%
8 661
 
1.0%
6 651
 
1.0%
7 600
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 58742
87.7%
1 2612
 
3.9%
2 968
 
1.4%
0 704
 
1.1%
5 703
 
1.0%
3 693
 
1.0%
4 677
 
1.0%
8 661
 
1.0%
6 651
 
1.0%
7 600
 
0.9%
Distinct388
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:52.195288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.510282348
Min length1

Characters and Unicode

Total characters63157
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)0.5%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 13290
73.9%
8 571
 
3.2%
13 419
 
2.3%
82 237
 
1.3%
50 184
 
1.0%
84 166
 
0.9%
5 125
 
0.7%
12 81
 
0.5%
14 73
 
0.4%
178 73
 
0.4%
Other values (378) 2773
 
15.4%
2024-01-27T14:09:52.531758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 53802
85.2%
1 2137
 
3.4%
8 1504
 
2.4%
2 1196
 
1.9%
3 1139
 
1.8%
5 827
 
1.3%
4 777
 
1.2%
0 735
 
1.2%
7 522
 
0.8%
6 518
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63157
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 53802
85.2%
1 2137
 
3.4%
8 1504
 
2.4%
2 1196
 
1.9%
3 1139
 
1.8%
5 827
 
1.3%
4 777
 
1.2%
0 735
 
1.2%
7 522
 
0.8%
6 518
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 63157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 53802
85.2%
1 2137
 
3.4%
8 1504
 
2.4%
2 1196
 
1.9%
3 1139
 
1.8%
5 827
 
1.3%
4 777
 
1.2%
0 735
 
1.2%
7 522
 
0.8%
6 518
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 53802
85.2%
1 2137
 
3.4%
8 1504
 
2.4%
2 1196
 
1.9%
3 1139
 
1.8%
5 827
 
1.3%
4 777
 
1.2%
0 735
 
1.2%
7 522
 
0.8%
6 518
 
0.8%
Distinct61
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:52.668228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.902901289
Min length1

Characters and Unicode

Total characters70221
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17327
96.3%
8 346
 
1.9%
5 39
 
0.2%
11 29
 
0.2%
20 27
 
0.2%
1 23
 
0.1%
38 18
 
0.1%
32 13
 
0.1%
81 11
 
0.1%
4 11
 
0.1%
Other values (51) 148
 
0.8%
2024-01-27T14:09:52.917476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 69324
98.7%
8 391
 
0.6%
1 155
 
0.2%
5 73
 
0.1%
2 71
 
0.1%
0 71
 
0.1%
3 51
 
0.1%
4 35
 
< 0.1%
6 35
 
< 0.1%
7 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70221
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 69324
98.7%
8 391
 
0.6%
1 155
 
0.2%
5 73
 
0.1%
2 71
 
0.1%
0 71
 
0.1%
3 51
 
0.1%
4 35
 
< 0.1%
6 35
 
< 0.1%
7 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70221
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 69324
98.7%
8 391
 
0.6%
1 155
 
0.2%
5 73
 
0.1%
2 71
 
0.1%
0 71
 
0.1%
3 51
 
0.1%
4 35
 
< 0.1%
6 35
 
< 0.1%
7 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 69324
98.7%
8 391
 
0.6%
1 155
 
0.2%
5 73
 
0.1%
2 71
 
0.1%
0 71
 
0.1%
3 51
 
0.1%
4 35
 
< 0.1%
6 35
 
< 0.1%
7 15
 
< 0.1%
Distinct102
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:53.037701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.535404624
Min length1

Characters and Unicode

Total characters63609
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 14816
82.3%
8 1492
 
8.3%
5 222
 
1.2%
20 107
 
0.6%
11 97
 
0.5%
12 92
 
0.5%
2 86
 
0.5%
6 78
 
0.4%
4 78
 
0.4%
56 77
 
0.4%
Other values (92) 847
 
4.7%
2024-01-27T14:09:53.279880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 59362
93.3%
8 1663
 
2.6%
1 644
 
1.0%
5 457
 
0.7%
2 445
 
0.7%
0 276
 
0.4%
3 241
 
0.4%
6 240
 
0.4%
4 187
 
0.3%
7 94
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 59362
93.3%
8 1663
 
2.6%
1 644
 
1.0%
5 457
 
0.7%
2 445
 
0.7%
0 276
 
0.4%
3 241
 
0.4%
6 240
 
0.4%
4 187
 
0.3%
7 94
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 63609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 59362
93.3%
8 1663
 
2.6%
1 644
 
1.0%
5 457
 
0.7%
2 445
 
0.7%
0 276
 
0.4%
3 241
 
0.4%
6 240
 
0.4%
4 187
 
0.3%
7 94
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 59362
93.3%
8 1663
 
2.6%
1 644
 
1.0%
5 457
 
0.7%
2 445
 
0.7%
0 276
 
0.4%
3 241
 
0.4%
6 240
 
0.4%
4 187
 
0.3%
7 94
 
0.1%
Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:53.392142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.636838595
Min length1

Characters and Unicode

Total characters65434
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 15537
86.4%
8 1173
 
6.5%
5 174
 
1.0%
11 87
 
0.5%
1 72
 
0.4%
20 68
 
0.4%
6 66
 
0.4%
14 63
 
0.4%
50 59
 
0.3%
38 55
 
0.3%
Other values (91) 638
 
3.5%
2024-01-27T14:09:53.624669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 62223
95.1%
8 1304
 
2.0%
1 535
 
0.8%
5 312
 
0.5%
2 237
 
0.4%
0 209
 
0.3%
3 187
 
0.3%
4 172
 
0.3%
6 171
 
0.3%
7 84
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65434
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 62223
95.1%
8 1304
 
2.0%
1 535
 
0.8%
5 312
 
0.5%
2 237
 
0.4%
0 209
 
0.3%
3 187
 
0.3%
4 172
 
0.3%
6 171
 
0.3%
7 84
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 65434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 62223
95.1%
8 1304
 
2.0%
1 535
 
0.8%
5 312
 
0.5%
2 237
 
0.4%
0 209
 
0.3%
3 187
 
0.3%
4 172
 
0.3%
6 171
 
0.3%
7 84
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 62223
95.1%
8 1304
 
2.0%
1 535
 
0.8%
5 312
 
0.5%
2 237
 
0.4%
0 209
 
0.3%
3 187
 
0.3%
4 172
 
0.3%
6 171
 
0.3%
7 84
 
0.1%
Distinct185
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:53.824487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.975377946
Min length1

Characters and Unicode

Total characters71525
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)0.7%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 17689
98.3%
84 26
 
0.1%
8 18
 
0.1%
309 10
 
0.1%
95 6
 
< 0.1%
227 4
 
< 0.1%
256 4
 
< 0.1%
87 4
 
< 0.1%
81 3
 
< 0.1%
377 3
 
< 0.1%
Other values (175) 225
 
1.3%
2024-01-27T14:09:54.154074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 70814
99.0%
2 120
 
0.2%
1 111
 
0.2%
8 93
 
0.1%
3 89
 
0.1%
4 74
 
0.1%
5 69
 
0.1%
7 56
 
0.1%
0 50
 
0.1%
6 49
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71525
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 70814
99.0%
2 120
 
0.2%
1 111
 
0.2%
8 93
 
0.1%
3 89
 
0.1%
4 74
 
0.1%
5 69
 
0.1%
7 56
 
0.1%
0 50
 
0.1%
6 49
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71525
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 70814
99.0%
2 120
 
0.2%
1 111
 
0.2%
8 93
 
0.1%
3 89
 
0.1%
4 74
 
0.1%
5 69
 
0.1%
7 56
 
0.1%
0 50
 
0.1%
6 49
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 70814
99.0%
2 120
 
0.2%
1 111
 
0.2%
8 93
 
0.1%
3 89
 
0.1%
4 74
 
0.1%
5 69
 
0.1%
7 56
 
0.1%
0 50
 
0.1%
6 49
 
0.1%
Distinct81
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:54.290740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.821865273
Min length1

Characters and Unicode

Total characters68763
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 16519
91.8%
11 54
 
0.3%
10 42
 
0.2%
9 41
 
0.2%
12 37
 
0.2%
8 32
 
0.2%
47 32
 
0.2%
44 31
 
0.2%
1 30
 
0.2%
3 29
 
0.2%
Other values (71) 1145
 
6.4%
2024-01-27T14:09:54.532199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 66220
96.3%
1 450
 
0.7%
4 381
 
0.6%
3 337
 
0.5%
2 324
 
0.5%
6 290
 
0.4%
5 277
 
0.4%
7 225
 
0.3%
0 133
 
0.2%
8 126
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68763
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 66220
96.3%
1 450
 
0.7%
4 381
 
0.6%
3 337
 
0.5%
2 324
 
0.5%
6 290
 
0.4%
5 277
 
0.4%
7 225
 
0.3%
0 133
 
0.2%
8 126
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 68763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 66220
96.3%
1 450
 
0.7%
4 381
 
0.6%
3 337
 
0.5%
2 324
 
0.5%
6 290
 
0.4%
5 277
 
0.4%
7 225
 
0.3%
0 133
 
0.2%
8 126
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 66220
96.3%
1 450
 
0.7%
4 381
 
0.6%
3 337
 
0.5%
2 324
 
0.5%
6 290
 
0.4%
5 277
 
0.4%
7 225
 
0.3%
0 133
 
0.2%
8 126
 
0.2%
Distinct343
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:54.741778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.849710983
Min length1

Characters and Unicode

Total characters69264
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)0.8%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 16422
91.3%
36 33
 
0.2%
42 27
 
0.2%
48 25
 
0.1%
37 24
 
0.1%
20 23
 
0.1%
45 23
 
0.1%
38 22
 
0.1%
35 22
 
0.1%
34 20
 
0.1%
Other values (333) 1351
 
7.5%
2024-01-27T14:09:55.062853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 65866
95.1%
2 608
 
0.9%
3 526
 
0.8%
4 497
 
0.7%
1 422
 
0.6%
5 361
 
0.5%
6 283
 
0.4%
8 255
 
0.4%
7 250
 
0.4%
0 196
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 65866
95.1%
2 608
 
0.9%
3 526
 
0.8%
4 497
 
0.7%
1 422
 
0.6%
5 361
 
0.5%
6 283
 
0.4%
8 255
 
0.4%
7 250
 
0.4%
0 196
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 65866
95.1%
2 608
 
0.9%
3 526
 
0.8%
4 497
 
0.7%
1 422
 
0.6%
5 361
 
0.5%
6 283
 
0.4%
8 255
 
0.4%
7 250
 
0.4%
0 196
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 65866
95.1%
2 608
 
0.9%
3 526
 
0.8%
4 497
 
0.7%
1 422
 
0.6%
5 361
 
0.5%
6 283
 
0.4%
8 255
 
0.4%
7 250
 
0.4%
0 196
 
0.3%
Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:55.186731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.848488217
Min length1

Characters and Unicode

Total characters69242
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row9999
2nd row9999
3rd row9999
4th row9999
5th row9999
ValueCountFrequency (%)
9999 16642
92.5%
38 200
 
1.1%
37 169
 
0.9%
35 163
 
0.9%
36 152
 
0.8%
39 141
 
0.8%
34 113
 
0.6%
33 81
 
0.5%
32 55
 
0.3%
40 47
 
0.3%
Other values (31) 229
 
1.3%
2024-01-27T14:09:55.408594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 66716
96.4%
3 1191
 
1.7%
4 226
 
0.3%
8 208
 
0.3%
7 184
 
0.3%
6 184
 
0.3%
5 183
 
0.3%
2 158
 
0.2%
1 130
 
0.2%
0 62
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69242
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 66716
96.4%
3 1191
 
1.7%
4 226
 
0.3%
8 208
 
0.3%
7 184
 
0.3%
6 184
 
0.3%
5 183
 
0.3%
2 158
 
0.2%
1 130
 
0.2%
0 62
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69242
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 66716
96.4%
3 1191
 
1.7%
4 226
 
0.3%
8 208
 
0.3%
7 184
 
0.3%
6 184
 
0.3%
5 183
 
0.3%
2 158
 
0.2%
1 130
 
0.2%
0 62
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 66716
96.4%
3 1191
 
1.7%
4 226
 
0.3%
8 208
 
0.3%
7 184
 
0.3%
6 184
 
0.3%
5 183
 
0.3%
2 158
 
0.2%
1 130
 
0.2%
0 62
 
0.1%

f_elx
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2301022677
Minimum0
Maximum1
Zeros13852
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:55.521118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4209098015
Coefficient of variation (CV)1.829229263
Kurtosis-0.354997655
Mean0.2301022677
Median Absolute Deviation (MAD)0
Skewness1.282591837
Sum4140
Variance0.177165061
MonotonicityNot monotonic
2024-01-27T14:09:55.617414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 13852
77.0%
1 4140
 
23.0%
ValueCountFrequency (%)
0 13852
77.0%
1 4140
 
23.0%
ValueCountFrequency (%)
1 4140
 
23.0%
0 13852
77.0%

f_mindef_mha
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.140729213
Minimum0
Maximum1
Zeros15460
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:55.713865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3477516686
Coefficient of variation (CV)2.471069518
Kurtosis2.270587085
Mean0.140729213
Median Absolute Deviation (MAD)0
Skewness2.066478816
Sum2532
Variance0.120931223
MonotonicityNot monotonic
2024-01-27T14:09:55.810465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 15460
85.9%
1 2532
 
14.1%
ValueCountFrequency (%)
0 15460
85.9%
1 2532
 
14.1%
ValueCountFrequency (%)
1 2532
 
14.1%
0 15460
85.9%

f_retail
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7567807915
Minimum0
Maximum1
Zeros4376
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:55.906914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.429038292
Coefficient of variation (CV)0.5669254517
Kurtosis-0.5669201011
Mean0.7567807915
Median Absolute Deviation (MAD)0
Skewness-1.197139478
Sum13616
Variance0.184073856
MonotonicityNot monotonic
2024-01-27T14:09:55.994947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 13616
75.7%
0 4376
 
24.3%
ValueCountFrequency (%)
0 4376
 
24.3%
1 13616
75.7%
ValueCountFrequency (%)
1 13616
75.7%
0 4376
 
24.3%

flg_affconnect_show_interest_ever
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing17497
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:56.091323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum495
Variance0
MonotonicityIncreasing
2024-01-27T14:09:56.179748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 495
 
2.8%
(Missing) 17497
97.2%
ValueCountFrequency (%)
1 495
2.8%
ValueCountFrequency (%)
1 495
2.8%

flg_affconnect_ready_to_buy_ever
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing17178
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:56.259785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum814
Variance0
MonotonicityIncreasing
2024-01-27T14:09:56.355991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 814
 
4.5%
(Missing) 17178
95.5%
ValueCountFrequency (%)
1 814
4.5%
ValueCountFrequency (%)
1 814
4.5%

flg_affconnect_lapse_ever
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)0.2%
Missing17178
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean0.01105651106
Minimum0
Maximum1
Zeros805
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:56.444577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1046313237
Coefficient of variation (CV)9.463321944
Kurtosis85.99027467
Mean0.01105651106
Median Absolute Deviation (MAD)0
Skewness9.369044869
Sum9
Variance0.0109477139
MonotonicityNot monotonic
2024-01-27T14:09:56.540821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 805
 
4.5%
1 9
 
0.1%
(Missing) 17178
95.5%
ValueCountFrequency (%)
0 805
4.5%
1 9
 
0.1%
ValueCountFrequency (%)
1 9
 
0.1%
0 805
4.5%

affcon_visit_days
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)2.5%
Missing17178
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean2.71007371
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:56.645984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum50
Range49
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.462605968
Coefficient of variation (CV)1.277679628
Kurtosis63.06397728
Mean2.71007371
Median Absolute Deviation (MAD)1
Skewness6.147896156
Sum2206
Variance11.98964009
MonotonicityNot monotonic
2024-01-27T14:09:56.758323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 396
 
2.2%
2 162
 
0.9%
3 99
 
0.6%
4 34
 
0.2%
5 34
 
0.2%
6 23
 
0.1%
7 14
 
0.1%
9 11
 
0.1%
8 10
 
0.1%
12 7
 
< 0.1%
Other values (10) 24
 
0.1%
(Missing) 17178
95.5%
ValueCountFrequency (%)
1 396
2.2%
2 162
0.9%
3 99
 
0.6%
4 34
 
0.2%
5 34
 
0.2%
ValueCountFrequency (%)
50 1
< 0.1%
40 1
< 0.1%
27 1
< 0.1%
19 1
< 0.1%
16 2
< 0.1%

n_months_since_visit_affcon
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.7%
Missing17178
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean2.158476658
Minimum0
Maximum5
Zeros219
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:56.862365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.74674412
Coefficient of variation (CV)0.8092485563
Kurtosis-1.405596609
Mean2.158476658
Median Absolute Deviation (MAD)2
Skewness0.1185560942
Sum1757
Variance3.05111502
MonotonicityNot monotonic
2024-01-27T14:09:56.950887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 219
 
1.2%
4 168
 
0.9%
1 127
 
0.7%
3 124
 
0.7%
2 98
 
0.5%
5 78
 
0.4%
(Missing) 17178
95.5%
ValueCountFrequency (%)
0 219
1.2%
1 127
0.7%
2 98
0.5%
3 124
0.7%
4 168
0.9%
ValueCountFrequency (%)
5 78
0.4%
4 168
0.9%
3 124
0.7%
2 98
0.5%
1 127
0.7%

clmcon_visit_days
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)4.3%
Missing17203
Missing (%)95.6%
Infinite0
Infinite (%)0.0%
Mean5.088719899
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:57.055006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile16.6
Maximum56
Range55
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.03477545
Coefficient of variation (CV)1.185912286
Kurtosis17.42456223
Mean5.088719899
Median Absolute Deviation (MAD)2
Skewness3.381011467
Sum4015
Variance36.41851473
MonotonicityNot monotonic
2024-01-27T14:09:57.175495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 230
 
1.3%
2 115
 
0.6%
3 90
 
0.5%
4 60
 
0.3%
5 56
 
0.3%
6 41
 
0.2%
9 35
 
0.2%
7 31
 
0.2%
8 27
 
0.2%
11 17
 
0.1%
Other values (24) 87
 
0.5%
(Missing) 17203
95.6%
ValueCountFrequency (%)
1 230
1.3%
2 115
0.6%
3 90
 
0.5%
4 60
 
0.3%
5 56
 
0.3%
ValueCountFrequency (%)
56 1
< 0.1%
52 1
< 0.1%
44 1
< 0.1%
43 1
< 0.1%
37 1
< 0.1%

recency_clmcon
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.8%
Missing17203
Missing (%)95.6%
Infinite0
Infinite (%)0.0%
Mean1.579214195
Minimum0
Maximum5
Zeros304
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:57.271928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.656512725
Coefficient of variation (CV)1.048947464
Kurtosis-0.7455776549
Mean1.579214195
Median Absolute Deviation (MAD)1
Skewness0.7194304225
Sum1246
Variance2.744034407
MonotonicityNot monotonic
2024-01-27T14:09:57.372082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 304
 
1.7%
1 145
 
0.8%
2 116
 
0.6%
3 90
 
0.5%
4 71
 
0.4%
5 63
 
0.4%
(Missing) 17203
95.6%
ValueCountFrequency (%)
0 304
1.7%
1 145
0.8%
2 116
 
0.6%
3 90
 
0.5%
4 71
 
0.4%
ValueCountFrequency (%)
5 63
0.4%
4 71
0.4%
3 90
0.5%
2 116
0.6%
1 145
0.8%

recency_clmcon_regis
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)11.5%
Missing17203
Missing (%)95.6%
Infinite0
Infinite (%)0.0%
Mean29.01901141
Minimum0
Maximum92
Zeros19
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:57.491016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q18
median21
Q347
95-th percentile75.6
Maximum92
Range92
Interquartile range (IQR)39

Descriptive statistics

Standard deviation24.64063322
Coefficient of variation (CV)0.8491203535
Kurtosis-0.5601744124
Mean29.01901141
Median Absolute Deviation (MAD)15
Skewness0.7819064283
Sum22896
Variance607.1608056
MonotonicityNot monotonic
2024-01-27T14:09:57.609937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 52
 
0.3%
21 27
 
0.2%
5 25
 
0.1%
2 23
 
0.1%
17 23
 
0.1%
4 22
 
0.1%
1 21
 
0.1%
15 21
 
0.1%
9 19
 
0.1%
0 19
 
0.1%
Other values (81) 537
 
3.0%
(Missing) 17203
95.6%
ValueCountFrequency (%)
0 19
0.1%
1 21
0.1%
2 23
0.1%
3 18
0.1%
4 22
0.1%
ValueCountFrequency (%)
92 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
89 4
< 0.1%
88 1
 
< 0.1%

hlthclaim_amt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16543
Missing (%)91.9%
Memory size281.1 KiB

recency_hlthclaim
Real number (ℝ)

MISSING 

Distinct125
Distinct (%)8.6%
Missing16543
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean41.66183575
Minimum0
Maximum126
Zeros83
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:57.738100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median32
Q368
95-th percentile111.6
Maximum126
Range126
Interquartile range (IQR)57

Descriptive statistics

Standard deviation35.23681035
Coefficient of variation (CV)0.84578151
Kurtosis-0.6846741209
Mean41.66183575
Median Absolute Deviation (MAD)25
Skewness0.6598812124
Sum60368
Variance1241.632804
MonotonicityNot monotonic
2024-01-27T14:09:57.866297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83
 
0.5%
1 46
 
0.3%
2 29
 
0.2%
7 29
 
0.2%
3 27
 
0.2%
24 26
 
0.1%
18 24
 
0.1%
5 24
 
0.1%
8 24
 
0.1%
22 24
 
0.1%
Other values (115) 1113
 
6.2%
(Missing) 16543
91.9%
ValueCountFrequency (%)
0 83
0.5%
1 46
0.3%
2 29
 
0.2%
3 27
 
0.2%
4 22
 
0.1%
ValueCountFrequency (%)
126 4
< 0.1%
125 1
 
< 0.1%
124 1
 
< 0.1%
123 8
< 0.1%
122 6
< 0.1%

hlthclaim_cnt_success
Real number (ℝ)

MISSING 

Distinct69
Distinct (%)5.4%
Missing16715
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean7.80266249
Minimum1
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:58.003218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q38
95-th percentile26
Maximum219
Range218
Interquartile range (IQR)6

Descriptive statistics

Standard deviation16.00563848
Coefficient of variation (CV)2.051304731
Kurtosis66.89531763
Mean7.80266249
Median Absolute Deviation (MAD)2
Skewness6.977092272
Sum9964
Variance256.1804631
MonotonicityNot monotonic
2024-01-27T14:09:58.116233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 295
 
1.6%
1 275
 
1.5%
4 153
 
0.9%
6 82
 
0.5%
3 72
 
0.4%
8 59
 
0.3%
5 45
 
0.3%
10 38
 
0.2%
12 35
 
0.2%
14 24
 
0.1%
Other values (59) 199
 
1.1%
(Missing) 16715
92.9%
ValueCountFrequency (%)
1 275
1.5%
2 295
1.6%
3 72
 
0.4%
4 153
0.9%
5 45
 
0.3%
ValueCountFrequency (%)
219 1
< 0.1%
203 1
< 0.1%
175 1
< 0.1%
161 1
< 0.1%
155 1
< 0.1%

recency_hlthclaim_success
Real number (ℝ)

MISSING 

Distinct125
Distinct (%)9.8%
Missing16715
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean40.87000783
Minimum0
Maximum126
Zeros79
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:58.244468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median31
Q367
95-th percentile111
Maximum126
Range126
Interquartile range (IQR)57

Descriptive statistics

Standard deviation35.08451977
Coefficient of variation (CV)0.8584417189
Kurtosis-0.6771072272
Mean40.87000783
Median Absolute Deviation (MAD)25
Skewness0.6709717917
Sum52191
Variance1230.923528
MonotonicityNot monotonic
2024-01-27T14:09:58.381159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
0.4%
1 44
 
0.2%
3 26
 
0.1%
7 26
 
0.1%
24 25
 
0.1%
2 24
 
0.1%
18 24
 
0.1%
4 22
 
0.1%
8 22
 
0.1%
6 21
 
0.1%
Other values (115) 964
 
5.4%
(Missing) 16715
92.9%
ValueCountFrequency (%)
0 79
0.4%
1 44
0.2%
2 24
 
0.1%
3 26
 
0.1%
4 22
 
0.1%
ValueCountFrequency (%)
126 1
 
< 0.1%
125 1
 
< 0.1%
124 1
 
< 0.1%
123 7
< 0.1%
122 5
< 0.1%

hlthclaim_cnt_unsuccess
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)2.8%
Missing17382
Missing (%)96.6%
Infinite0
Infinite (%)0.0%
Mean2.455737705
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:58.493829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum68
Range67
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.694247274
Coefficient of variation (CV)1.504333002
Kurtosis173.8006147
Mean2.455737705
Median Absolute Deviation (MAD)1
Skewness11.15207887
Sum1498
Variance13.64746292
MonotonicityNot monotonic
2024-01-27T14:09:58.793433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 267
 
1.5%
2 180
 
1.0%
4 70
 
0.4%
3 51
 
0.3%
6 12
 
0.1%
5 10
 
0.1%
12 3
 
< 0.1%
10 3
 
< 0.1%
8 3
 
< 0.1%
7 3
 
< 0.1%
Other values (7) 8
 
< 0.1%
(Missing) 17382
96.6%
ValueCountFrequency (%)
1 267
1.5%
2 180
1.0%
3 51
 
0.3%
4 70
 
0.4%
5 10
 
0.1%
ValueCountFrequency (%)
68 1
< 0.1%
33 1
< 0.1%
23 2
< 0.1%
19 1
< 0.1%
17 1
< 0.1%

recency_hlthclaim_unsuccess
Real number (ℝ)

MISSING 

Distinct123
Distinct (%)20.2%
Missing17382
Missing (%)96.6%
Infinite0
Infinite (%)0.0%
Mean49.40983607
Minimum0
Maximum126
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:58.912421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q118
median45
Q378
95-th percentile112
Maximum126
Range126
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.93224831
Coefficient of variation (CV)0.7069897634
Kurtosis-0.9378278107
Mean49.40983607
Median Absolute Deviation (MAD)29
Skewness0.385107963
Sum30140
Variance1220.261972
MonotonicityNot monotonic
2024-01-27T14:09:59.040919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
0.1%
0 12
 
0.1%
22 11
 
0.1%
5 11
 
0.1%
8 10
 
0.1%
79 9
 
0.1%
18 9
 
0.1%
14 9
 
0.1%
16 9
 
0.1%
47 9
 
0.1%
Other values (113) 508
 
2.8%
(Missing) 17382
96.6%
ValueCountFrequency (%)
0 12
0.1%
1 13
0.1%
2 9
0.1%
3 5
 
< 0.1%
4 7
< 0.1%
ValueCountFrequency (%)
126 5
< 0.1%
125 1
 
< 0.1%
123 3
< 0.1%
122 1
 
< 0.1%
121 1
 
< 0.1%

flg_hlthclaim_839f8a_ever
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing17707
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:59.145727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum285
Variance0
MonotonicityIncreasing
2024-01-27T14:09:59.242136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 285
 
1.6%
(Missing) 17707
98.4%
ValueCountFrequency (%)
1 285
1.6%
ValueCountFrequency (%)
1 285
1.6%

recency_hlthclaim_839f8a
Real number (ℝ)

MISSING 

Distinct104
Distinct (%)36.5%
Missing17707
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean46.07719298
Minimum0
Maximum124
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:59.353889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q118
median42
Q372
95-th percentile110
Maximum124
Range124
Interquartile range (IQR)54

Descriptive statistics

Standard deviation34.28804568
Coefficient of variation (CV)0.7441435439
Kurtosis-0.7414638714
Mean46.07719298
Median Absolute Deviation (MAD)26
Skewness0.5138004458
Sum13132
Variance1175.670077
MonotonicityNot monotonic
2024-01-27T14:09:59.490540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
0.1%
18 10
 
0.1%
2 8
 
< 0.1%
24 7
 
< 0.1%
35 7
 
< 0.1%
1 7
 
< 0.1%
54 6
 
< 0.1%
19 5
 
< 0.1%
7 5
 
< 0.1%
49 5
 
< 0.1%
Other values (94) 214
 
1.2%
(Missing) 17707
98.4%
ValueCountFrequency (%)
0 11
0.1%
1 7
< 0.1%
2 8
< 0.1%
3 2
 
< 0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
124 3
< 0.1%
123 2
< 0.1%
122 1
 
< 0.1%
120 2
< 0.1%
118 1
 
< 0.1%

flg_hlthclaim_14cb37_ever
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing16617
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:59.594686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum1375
Variance0
MonotonicityIncreasing
2024-01-27T14:09:59.675227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 1375
 
7.6%
(Missing) 16617
92.4%
ValueCountFrequency (%)
1 1375
7.6%
ValueCountFrequency (%)
1 1375
7.6%

recency_hlthclaim_14cb37
Real number (ℝ)

MISSING 

Distinct125
Distinct (%)9.1%
Missing16617
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean42.176
Minimum0
Maximum126
Zeros81
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:09:59.803925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median33
Q368
95-th percentile112
Maximum126
Range126
Interquartile range (IQR)57

Descriptive statistics

Standard deviation35.54871811
Coefficient of variation (CV)0.8428660403
Kurtosis-0.7113867046
Mean42.176
Median Absolute Deviation (MAD)26
Skewness0.6445952181
Sum57992
Variance1263.71136
MonotonicityNot monotonic
2024-01-27T14:09:59.940425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
0.5%
1 47
 
0.3%
2 26
 
0.1%
3 25
 
0.1%
22 24
 
0.1%
8 24
 
0.1%
24 24
 
0.1%
18 23
 
0.1%
7 23
 
0.1%
5 23
 
0.1%
Other values (115) 1055
 
5.9%
(Missing) 16617
92.4%
ValueCountFrequency (%)
0 81
0.5%
1 47
0.3%
2 26
 
0.1%
3 25
 
0.1%
4 21
 
0.1%
ValueCountFrequency (%)
126 4
< 0.1%
125 1
 
< 0.1%
124 1
 
< 0.1%
123 9
0.1%
122 6
< 0.1%

giclaim_amt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17544
Missing (%)97.5%
Memory size281.1 KiB

recency_giclaim
Real number (ℝ)

MISSING 

Distinct104
Distinct (%)23.2%
Missing17544
Missing (%)97.5%
Infinite0
Infinite (%)0.0%
Mean39.99776786
Minimum0
Maximum144
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:10:00.077642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median40
Q358
95-th percentile93
Maximum144
Range144
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.32925205
Coefficient of variation (CV)0.7582736155
Kurtosis-0.09354442188
Mean39.99776786
Median Absolute Deviation (MAD)26
Skewness0.647944067
Sum17919
Variance919.8635297
MonotonicityNot monotonic
2024-01-27T14:10:00.230106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 15
 
0.1%
4 12
 
0.1%
0 12
 
0.1%
39 12
 
0.1%
3 12
 
0.1%
52 12
 
0.1%
10 11
 
0.1%
5 11
 
0.1%
13 10
 
0.1%
18 9
 
0.1%
Other values (94) 332
 
1.8%
(Missing) 17544
97.5%
ValueCountFrequency (%)
0 12
0.1%
1 8
< 0.1%
2 4
 
< 0.1%
3 12
0.1%
4 12
0.1%
ValueCountFrequency (%)
144 1
 
< 0.1%
125 3
< 0.1%
124 2
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%

giclaim_cnt_success
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

recency_giclaim_success
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

giclaim_cnt_unsuccess
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

recency_giclaim_unsuccess
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

flg_gi_claim_29d435_ever
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

flg_gi_claim_058815_ever
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

flg_gi_claim_42e115_ever
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

flg_gi_claim_856320_ever
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17992
Missing (%)100.0%
Memory size281.1 KiB

f_purchase_lh
Real number (ℝ)

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing17282
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.1 KiB
2024-01-27T14:10:00.342771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum710
Variance0
MonotonicityIncreasing
2024-01-27T14:10:00.430982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 710
 
3.9%
(Missing) 17282
96.1%
ValueCountFrequency (%)
1 710
3.9%
ValueCountFrequency (%)
1 710
3.9%